First Draft of Narketpally syndrome and the embedding of contextual values in real life patient pathways

Title:

Narketpally syndrome and the embedding of contextual values in real life patient pathways

TLDR summary of the entire article for covering letter:

This article from Narketpally talks about a unique syndromic approach to research design that is geared to capture contextual patient data with precision from individual patient participants treating each patient as a separate project.The data is processed through Web 2.0-3.0 driven case based reasoning, which finally generates learning outcomes that  improves the same patient's as well as similar patient's illness outcomes with precision as opposed to population based approaches that focus on improving accuracy around average patient population data.

Abstract:


I: This paper commentary provides an illustration of a global patient centered learning ecosystem with it's epicenter at Narketpally that adopts a syndromic approach to medical education and research for the sake of each of it's primary beneficiaries (patients).
M:To illustrate it's syndromic collective cognitive workflow, the paper is designed as a team based learning exercise using our prior published Web 2.0 medical cognition tools such as CBBLE (pronounced cable), which stands for case based blended learning ecosystem and PaJR (patients journey record) as well as Web 3.0 medical cognitive tools in the background of three papers, one by Sturmberg et al and two commentaries by Greenhalgh and Ioannidis.
R: Thematic learning outcomes emerged as a result of our engagement in the commentary amidst our day to day CBBLE PaJR workflow. Influenced by Sturmbergs paper, we recognised (from our daily practice logged in online learning portfolios) that individual PaJR patient connections may drive contextual learning as well as real patient illness outcomes. We identified a population based data driven value model and termed it "Wildebeest river crossing value model" inspired by our ongoing CBBLE journal clubs as well as Greenhalgh's commentary. Finally from Ioannidis's commentary the way forward appeared to point at an integrative model promoting not only population data driven resource intensive Phase 2 and phase 3 clinical trials but also relatively less resource intensive individual patient centred outcomes research that can be explored further in phase 1 and phase 4 of the current hierarchy of clinical trials that can easily be carried out even in low resource academic institutions.
D: Narketpally individual patient syndromic approach to medical education and research borrows from the etymological origins of 'syndrome' where it means 'together we flow' and reiterates that research is an academic tool to solve real life problems while we flow together in a stream of collective cognition and address each other's regular individual local as well as non local (aka quantum entangled) requirements. There is an increasing need to first distinguish individual person and patient centered medical education research from currently dominant population based paradigms. There is a need to utilise collective medical cognition tools to re-establish contextual values in real life patient pathways. This can primarily begin by viewing every patient as a separate research project literally not missing a single tree detail (individual patient centred medicine) even if the forest canopy itself looks good enough (population centred medicine).

Key words, phrases: Syndrome etymology, Collective medical cognition, 

Patient journey record PaJR, Case based blended learning ecosystem CBBLE (cable), User driven healthcare, UDHC, 

Critical realist heutagogy CRH, User driven learning community ontologies UDLCOs, AI driven case based reasoning CBR engines, Tree canopy (population based medicine, phase 2-3 clinical trials) stem connected to grounded tree roots (individual patient centered medicine, phase 1-4 trials), Translating positive learning outcomes to positive illness outcomes, Being together in the flow regularly with our patients and their journeys, Non local Quantum entangled theory of everything aka Everything is connected, Human ontologies as life to death trajectories connected through UDLCOs, Cyclical evolution and dissolution, Whole-o-graphic systems where every fractal or factal broken through reductionism still shows the whole , Those who know the whole actually don't and those who don't know it actually do (Kena upanishad and it's subsequent variations from Zen to critical realism), Researcher as a witness to the whole stream/river of consciousness with collective compassion 

Introduction


We try to illustrate a working solution to the current problem statement around fragmentation of medical education, research and practice bereft of contextual connections between it's real life primary beneficiaries (patients) and secondary beneficiaries (healthcare professionals) as aptly highlighted in a recent work by Sturmberg et al. (2025) (Ref 1 https://onlinelibrary.wiley.com/doi/10.1111/jep.14139) and subsequently already commented on by Greenhalgh (2
https://pmc.ncbi.nlm.nih.gov/articles/PMC11635397/) as well as Ionnidis (3 https://onlinelibrary.wiley.com/doi/full/10.1111/jep.70027) and our final conclusions take into account all the three papers as well as our current regular workflow and past archived work on this topic.


Our paper addresses the limitations of current scientific research that often compels one to look at single causes and effects due to the inherent reductionist nature of scientific study designs that as highlighted by Sturmberg and Mercuri, often manages to remove the individual patient context embedded in a greater whole that the scientific study is trying to address!


We want to pivot this write up on our own patient centred medical education, research and practice experiences currently in location Narketpally since seven years, to illustrate how we have been living daily with what Sturmberg et al write about and how we utilise our medical cognition case based reasoning solutions to tackle medical education, research and practice challenges and for all of that, first we would like to, with our readers, rhetorically explore the term 'syndrome.'


Syndrome is a word from mid 16th century: modern Latin, which is in turn from Greek sundromē, from sun- ‘together’ + dramein ‘to run or flow’, which appears to be further derived from Sanskrit 'sam' and "drroom" that have similar meaning as the Greek, meaning "together we flow!" (Ref 4 https://www.etymonline.com/word/syndrome). In Sanskrit "drroom" or "druma" also means a tree (ref 5: https://www.wisdomlib.org/definition/druma)
suggesting a flow of the river where it's tributaries form branches draining different locations before it joins the mainstream/stem!
In different places in the paper we try to use a visual imagery, we used earlier, of a river where our 'together we flow' is symbolized in different elements of a running rivulet(Ref 6:
Hematology: the river within https://www.researchgate.net/publication/336983518_Hematology_The_River_Within). The write up also uses a "forest of trees" imagery that if looked at from a population based medical cognition and policy making lens, would be akin to viewing the forest canopy. If viewed from an individual patient centred medical cognition perspective, it would appear to explore detailed connections in each tree and it's branches as well as it's connections between it's roots through the wood wide web (ref 7: Giovannetti, Manuela; Avio, Luciano; Fortuna, Paola; Pellegrino, Elisa; Sbrana, Cristiana; Strani, Patrizia (2006). "At the Root of the Wood Wide Web". Plant Signaling & Behavior. 1 (1): 1–5.)






Figure 1: A syndromic, together we flow (sama druma) approach to integrate research designs (made using Canva)


We shared the above introduction with a native English speaker and author and got the response which we quote below:
"I think your work is very important and needs to be highlighted and repeated. I know English is not your first, second, or maybe even your third language. I also know there is a premium to sounding academic. However your draft contains many run-on sentences that I believe make it more difficult to grasp the points you are trying to make. I hope you or someone can edit it for clarity….maybe AI."


AI LLM Deep Seek's restructured version:


Here’s a restructured version of your commentary in polished academic English, addressing clarity, concision, and flow while preserving your core ideas:


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**Introduction**


The fragmentation of medical education, research, and practice—and its disconnection from the contextual needs of primary beneficiaries (patients) and secondary beneficiaries (healthcare professionals)—remains a critical challenge in modern healthcare. This issue, recently articulated by Sturmberg et al. (2025) (1), has been further examined in commentaries by Greenhalgh (2) and Ioannidis (3). Building on these works, our paper proposes a patient-centered framework grounded in seven years of experiential practice and research in Narketpally, India. We integrate insights from these scholars with our archived workflow data to demonstrate how case-based medical cognition and systems thinking can bridge this fragmentation.


**Limitations of Reductionist Paradigms**


Contemporary scientific research often prioritizes reductionist methodologies that isolate single causes and effects, inadvertently stripping away the holistic context of individual patients. As Sturmberg and Mercuri (1) emphasize, such approaches neglect the interconnectedness of biological, social, and environmental factors inherent to clinical practice. Our work seeks to recenter this "greater whole," advocating for study designs and clinical frameworks that preserve patient narratives and systemic interdependencies.


**A Syndromic Approach to Integration**


To contextualize our framework, we first explore the etymology of the term *syndrome*. Derived from the Greek *sundromē* (*sun-*, "together"; *dramein*, "to flow") and linked etymologically to the Sanskrit *sam* ("together") and *druma* ("tree" or "flowing river"), the term evokes a confluence of tributaries merging into a unified stream (4, 5). This metaphor aligns with our conceptual model of healthcare as a dynamic system:


1. **The River Imagery**: We adopt the visual metaphor of a river (6) to represent the integration of medical education, research, and practice. Like tributaries feeding a main channel, discrete clinical insights and patient experiences coalesce into a cohesive flow of knowledge.
2. **The Tree Imagery**: Complementing this, the Sanskrit *druma* ("tree") reflects the dual lenses of healthcare analysis:
- A **forest canopy perspective** (population-level policy and epidemiology)
- A **root-and-branch perspective** (individual patient-provider interactions and pathophysiological pathways)
- A **"Wood Wide Web" perspective** (7), mirroring the interconnected fungal networks in forests, to symbolize the latent synergies between stakeholders in healthcare ecosystems.


**Case-Based Reasoning in Practice**


Drawing on our Narketpally experience, we illustrate how case-based reasoning—anchored in these syndromic metaphors—addresses real-world complexities. By embedding patient narratives into medical education and research, we demonstrate how contextualized, systems-oriented frameworks can harmonize reductionist rigor with holistic relevance.


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**Key Improvements**:
1. **Structure**: Divided dense paragraphs into focused sections with subheadings for readability.
2. **Clarity**: Simplified run-on sentences, tightened phrasing, and clarified conceptual links (e.g., explicit connection between metaphors and practical frameworks).
3. **Academic Tone**: Maintained precision while avoiding overly complex syntax.
4. **Flow**: Used transitional phrases ("Building on these works," "To contextualize") to guide readers through the argument.
5. **Citations**: Standardized in-text references (1, 2, 3) without URLs, consistent with most academic styles.

2nd draft after listing and removing the reference links:

Introduction

Medical education, research, and practice are increasingly criticized for their fragmentation and for lacking meaningful connections with their primary beneficiaries—patients—and secondary stakeholders—healthcare professionals. This concern has been articulated in recent works by Sturmberg et al.¹ and further examined in commentaries by Greenhalgh² and Ioannidis³. Our paper presents a working model to address this fragmentation: a syndromic, patient-centered, practice-integrated approach developed and implemented in Narketpally, India, over the past seven years.

While mainstream scientific research tends to adopt a reductionist paradigm, isolating single causes and effects, this often strips away the real-world complexity and context that shape a patient’s health journey. As Sturmberg and Mercuri¹ argue, this form of inquiry often neglects the “greater whole”—the interconnected biological, social, and systemic factors that influence illness and healing.

Our work challenges this model by illustrating how case-based blended learning ecosystems (CBBLE) and patient journey records (PaJR) can support a “syndromic” approach to medical cognition, drawing on real-time patient narratives, team-based reflection, and critical realist heuristics to guide both care and learning. By “syndromic,” we return to the original etymology of the term—Greek: sundromē, “together we flow”—and its Sanskrit parallels (sam and druma) that denote collective movement and branching interconnectedness⁴⁻⁵.

To visualize this, we draw from previous metaphors we have used: the river, representing the continuous flow of collective medical cognition, and the tree, representing both the forest canopy of population-based medicine and the root-level intricacies of individual patient experience⁶⁻⁷. Our model sees every patient not just as a subject in a system, but as a living research project, where the learning flows both from practice to research and from research back to practice.

This paper integrates insights from the aforementioned scholarly commentaries¹⁻³ with our own archived casework, real-time collaborative learning, and ongoing online CBBLE-PaJR workflows, in order to explore how contextual, person-centered approaches can generate new knowledge, optimize patient outcomes, and reimagine research designs in both resource-limited and digitally interconnected settings.

Methodology:

True to our regular research technique of employing a person centered contextual syndromic approach to each and every patient (ref 8 : https://sites.pitt.edu/~super1/lecture/lec53081/001.htm), that we encounter in our case based blended learning ecosystem CBBLE (as opposed to the mainstream population based approach emphasized in Ioannidis's commentary), we took into account, inputs from every individual node that is a part of our user driven healthcare CBBLE. (Ref 9 https://pmc.ncbi.nlm.nih.gov/articles/PMC6163835/) 

Our CBBLE's individual 'patient centered research outcomes' project work 'flow,' is engaged everyday in optimizing individual clinical complexity using medical cognition tools (ref 10 Samitinjay et al JECP UK https://pubmed.ncbi.nlm.nih.gov/37335625/, ref 13 https://userdrivenhealthcare.blogspot.com/2022/10/medical-cognition-tools-to-resolve.html?m=1). Hence , this commentary is a collaborative response from members of the Narketpally CBBLE/cable, particularly those who are regular respondents in it's online learning arm. Drawn from clinicians, researchers, and AI tool users in disparate geographies, their responses
converged into an integrative cognitive process: part review, part discussion, part reimagination and is a recent addition to our previously published similar collective conversational commentaries (ref 14,15)


14): https://pmc.ncbi.nlm.nih.gov/articles/PMC6103343/


15) https://journals.lww.com/mjdy/fulltext/2019/12030/invited_commentary__a_patient_of_pulmonary.20.aspx


To address individual unique requirements that get missed in generalizeable studies, Narketpally CBBLE routinely uses, Patient Journey Record (PaJR), a user-driven healthcare interface originally begun for patients and health professionals to collaboratively document individual illness journeys through a shared platform in an attempt to optimise their individual clinical complexity. (12,16)
Ref 16, Patient Journey Record Systems (PaJR): The Development of a Conceptual Framework for a Patient Journey System. In R. Biswas, & C. Martin (Eds.), User-Driven Healthcare and Narrative Medicine: Utilizing Collaborative Social Networks and Technologies (pp. 75-92). Hershey, PA: Medical Information Science Reference. doi:10.4018/978-1-60960-097-6.ch006 at http://www.igi-global.com/chapter/patient-journey-record-systems-pajr/49246


Our overall daily activity as physician faculty and students is largely around encountering real patients in the general medicine department's outpatient-inpatient, brick and mortar hospital settings followed by blending it with our online global team based learning, which is why we call it a CBBLE (cable) and this cable doesn't belong to a single online group but can connect to diverse patient centred online learning groups globally, from which we often extract learning outcomes using a technique called "critical realist heutagogy" CRH and archive them as "user driven learning community ontologies" UDLCOs published and detailed earlier (ref 17: glossary https://userdrivenhealthcare.blogspot.com/2023/11/glossary-of-user-driven-healthcare.html?m=1, ref 18 podcast on the user driven healthcare glossary: https://youtu.be/EhfMlwgx_Tw?feature=shared detailing our current Web 2.0 to 3.0 medical cognition journey along with podcast on our past web 1.0 to web 2.0 medical cognition journey here: https://youtu.be/f4YrTCsiBxc?feature=shared (ref 19) and in general Web 0.0-current medical cognition journey (ref 20) here: https://youtu.be/tjqm4YMbpEo?feature=shared)


This team based learning continues in a PaJR group created for each patient where patient is deidentified meticulously as per The Health Insurance Portability and Accountability Act HIPAA guidelines (Ref: 21 https://www.cdc.gov/phlp/php/resources/health-insurance-portability-and-accountability-act-of-1996-hipaa.html)
and the patient advocate keeps sharing the daily data of the patient there with the team in the online PaJR group and all interactions as well the patient's illness events trajectory is captured and archived in the cloud as a PaJR case report electronic health record EHR after obtaining their signed informed consent. One can easily access these individual online learning portfolios of our ground working team members (formal and informal elective medical students) from our dashboard Ref 22: https://medicinedepartment.blogspot.com/2022/02/?m=1)

The PaJR group is ideally meant to offer life long learning support and informational continuity for that patient while we also try to analyse how our team based patient centered learning outcomes influence his her illness outcomes and how our past similar PaJR patients or other's similar patients (journal published case reports) influence his her illness outcomes. Not all patients that we encounter offline gets discussed online and not all patients that we may superficially present in our online CBBLE get's PaJRed for further informational continuity and this may happen due to various factors involving patient consent and sensitive patient data confidentiality to lack of adequately trained manpower for data capture at that time. We are still working on how to optimise our data capture and processing and it may take up another very large paper on it's own.


This commentary writing began with an invite to the corresponding author from editor JECP, UK and subsequently the corresponding author, with the editor's permission, invited other potential CBBLE author team members for a syndromic (together we flow) approach. All of them 'got together' in online fora with every participant rereading and randomly tossing each other words and sentences from the article and it's two commentaries to generate new ideas of their own along with a shared analysis of the regular local Narketpally individual PaJR patient processing through our CBBLE using the usual web 2.0 and more recently web 3.0 medical cognition tools(ref 17,18 glossary).

2nd draft after listing and removing the reference links:

Methodology:

True to our regular research technique of employing a person centered contextual

syndromic approach to each and every patient 8, that we encounter in our case based blended learning ecosystem CBBLE (as opposed to the mainstream population based approach emphasized in Ioannidis's commentary), we took into account, inputs from every individual node that is a part of our user driven healthcare CBBLE. Our CBBLE's individual 'patient-centered research outcomes' project work 'flow' is engaged every day in optimizing individual clinical complexity using medical cognition tools9,10. Hence, this commentary is a collaborative response from members of the Narketpally CBBLE/Cable, particularly those who are regular respondents in its online learning arm.

Drawn from clinicians, researchers, and AI tool users in disparate geographies, their

responses converged into an integrative cognitive process: part review, part discussion, part reimagination, and is a recent addition to our previously published similar collective conversational commentaries11,12.

To address individual unique requirements that get missed in generalizable studies,

Narketpally CBBLE routinely uses, Patient Journey Record (PaJR), a user-driven

healthcare interface originally begun for patients and health professionals to

collaboratively document individual illness journeys through a shared platform in an

attempt to optimise their individual clinical complexity 9,13.

Our overall daily activity as physician faculty and students is largely around

encountering real patients in the general medicine department's outpatient-inpatient,

brick and mortar hospital settings followed by blending it with our online global team

based learning, which is why we call it a CBBLE (cable) and this cable doesn't belong to a single online group but can connect to diverse patient centred online learning groups globally, from which we often extract learning outcomes using a technique called "critical realist heutagogy" CRH and archive them as "user driven learning community

ontologies" UDLCOs published and detailed earlier 14, podcast on the user driven

healthcare glossary 15 detailing our current Web 2.0 to 3.0 medical cognition journey

along with podcast on our past web 1.0 to web 2.0 medical cognition journey 16 and in

general Web 0.0-current medical cognition journey 17.

This team-based learning continues in a PaJR group created for each patient, where a

patient is deidentified meticulously as per The Health Insurance Portability and

Accountability Act HIPAA guidelines 18 and the patient advocate keeps sharing the daily

data of the patient there with the team in the online PaJR group and all interactions as

well the patient's illness events trajectory is captured and archived in the cloud as a

PaJR case report electronic health record EHR after obtaining their signed informed

consent. One can easily access these individual online learning portfolios of our ground working team members (formal and informal elective medical students) from our

dashboard 19.

The PaJR group is ideally meant to offer life long learning support and informational

continuity for that patient while we also try to analyse how our team based patient

centered learning outcomes influence his her illness outcomes and how our past similar PaJR patients or other's similar patients (journal published case reports) influence his her illness outcomes. Not all patients that we encounter offline get discussed online, and not all patients that we may superficially present in our online CBBLE get's PaJRed for further informational continuity and this may happen due to various factors involving patient consent and sensitive patient data confidentiality to lack of adequately trained manpower for data capture at that time. We are still working on how to optimise our data

capture and processing, and it may take up another very large paper on its own.

This commentary writing began with an invitation to the corresponding author from the editor JECP, UK, and subsequently, the corresponding author, with the editor's

permission, invited other potential CBBLE author team members for a syndromic

(together we flow) approach. All of them 'got together' in online fora with every participant rereading and randomly tossing each other words and sentences from the

article and it's two commentaries to generate new ideas of their own along with a shared

analysis of the regular local Narketpally individual PaJR patient processing through our CBBLE using the usual web 2.0 and more recently web 3.0 medical cognition tools 14,15.

Sample some of the Socratic discussions between the authors here:

01-05-2025 23:31] RK: I think I understand terms, it's just presented in a very haphazard way that are not making sense from a paper point of view. Thoughts need to be organized so that anyone is able to understand. 

Also I'm missing evidence in the paper that indeed anything has been proven conclusively. If the goal is to present a method, not evidence of the efficacy then the content suffices. But if we are claiming our case studies prove our method then needs better explanation.

02-05-2025 08:11] RB: Let me take the word 'haphazard' here to pivot our understanding of 'syndrome' and how it was entrenched in individual patient centered medical cognition and then nearly driven out by reductionist public health canopy approaches to medical cognition, which reused the term in a different manner as a stop gap approach to medical uncertainty.

Most medical students are introduced to the concept of 'syndrome' when they meet certain diagnosis entities in medicine where the diagnosis is mentioned as a syndrome rather than a disease, for example nephrotic syndrome which is a constellation of apparently "haphazardly"  arranged data points ranging from symptoms such as edema, signs such as proteinuria and where even after the renal biopsy the cause remains elusive and we can only name it based on further special signs arising from special processing of the tissue where we may find deposits of certain immunoglobulin proteins such as IgA, which may have a host of causes on it's own! @~VP @S @A may be able to relate to it better from their own PaJR patient experiences with similar patients. So a syndrome is largely particularly driven by population based medicine understood as a stop gap measure to depict haphazard, complex, chaotic but real data points till their uncertainty can be resolved well enough to label them as disease, particularly if there is a single cause attributable to a single effect, for example Fabry's disease that is known to cause nephrotic syndrome or diabetes or SLE but essentially even the above so called diseases, if dug deeper will always reveal layers of uncertainty that may still (depending on our vantage point such as if we are perched on the canopy or on the ground), provide evidence of residual uncertainty that tells us that we are essentially still flowing together in a syndromic manner till we reach singularity!

https://pajrcasereporter.blogspot.com/2025/03/conversational-commentary-on-contextual.html?m=1

Results:

In brief divided into thematic learning outcomes as below:

1) Cabled in user driven learning workflow outcomes from population based medical cognition (research fora and journal club driven CRH and UDLCOs)

Sample a) Wildebeest model of EBM values 

Sample b) Hypothesis: Rote memorization driven big pharma promotion 

2) CBBLE (cable) user driven learning workflow outcomes from individual patient centered medical cognition (real PaJR patient driven CRH and UDLCOs)  

Sample a) Out patient external medicine connections and outcomes

Sample b) In patient internal medicine connections and outcomes

Detailed narrative of the results:

One of the first result of our regular methodological reflections around our work and the three paper authors in the last few months was that from the themes that emerged, we could divide medical education, research and practice into two major cognitive flows. Of these one was driven by population based researchers , powerful policy makers and managers often working top down high above the forest canopy , efficiently doing things right (ref 1 and 19https://www.saudercpa.com/blog/2024/02/10/peter-druckers-timeless-management-wisdom-ten-quotes-to-lead-and-inspire/#:~:text=Drucker%20(%20Peter%20Drucker%20)%20's%20distinction,about%20setting%20the%20right%20direction%20and%20priorities.)
with a few well funded research assistants working for them on ground regularly supplying them controlled experimental data that by design may not need digging deeper into the roots.


The other flow in our collective medical cognition space appears driven by individual patient physicians, aka doctor practitioners who are largely ground level workers doing the right thing with a goal toward effectiveness rather than efficiency.


Doctor practitioners regularly and directly interface with patients and build up their experiential knowledge base one patient at a time. These individual doctor patient encounter experiences are largely captured in individual memories and if expressed directly from memory would be rightly labeled anecdotal although sometimes some of these doctors also share their real patient data captured as clinical case reports logically lowest in the hierarchy that consume very low resources (analogous to phase 1 and phase 4 in clinical trial parlance) in comparison to population based medicine researchers who are higher above in the canopy and have more structured high resource tools to set up well randomized experiments (phase 2 and phase 3 clinical trials), unbiased by ground truths and well designed to control for real life confounders scattered chaotically on the ground.


Most doctor practitioners are also trained in population based medical cognition through current medical education and research training systems and our group too continues to regularly dabble in it using medical cognition tools created with the flow such as critical realist heutagogy (ref 17,18). Population based medicine learning exercises by our group is often done through journal clubs around published randomised controlled trials that we discuss in various online groups we are cabled in. For a larger part of our day, we continue to engage in self directed learning around our individual real patient practice and hence the results of our regular workflow exercise are divided into thematic learning outcomes as below:


1) Cabled in user driven learning workflow outcomes from population based medical cognition (research fora and journal club driven CRH and UDLCOs)


2) CBBLE (cable) user driven learning workflow outcomes from individual patient centered medical cognition (real PaJR patient driven CRH and UDLCOs)


The Influence of the article and it's two commentaries in shaping our learning outcomes and CRH driven UDLCOs during the time taken to write this paper are also factored into the above. We haven't counted the number of CRH driven UDLCOs we created during these few months but they are available open access for both quantitative and qualitative analysis in our archived online learning portfolios (Ref 20 https://medicinedepartment.blogspot.com/2022/02/?m=1, ref 21 https://pajrcasereporter.blogspot.com/?m=1, ref 22 https://userdrivenhealthcare.blogspot.com/?m=1)




1) Population based research phase 2-3 clinical trials journal club learning outcomes:


a) The Wildebeest model of EBM values was inspired by the commentary on EBM values! (Ref 23 : https://medicinedepartment.blogspot.com/2025/04/udlco-crh-wildebeest-model-of-ebm.html?m=1 and ref 2)


The most difficult aspect of the EBM definition which states that it "combines the best available evidence with clinical expertise and patient values in order to provide the best care for the individual patient," is the part about how to combine patient values. The current population centred average outcomes research paradigm is focused on values that have been described below:


One of our CBBLE (cable) conversations logged on April 3 revolves around a journal club discussion on a recent study comparing the efficacy of clopidogrel versus aspirin monotherapy in high-risk patients post-PCI (percutaneous coronary intervention). (Ref 24 : https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(25)00449-0/abstract#:~:text=Among%20patients%20who%20were%20at,myocardial%20infarction%2C%20and%20stroke%2C%20without)
While the original posters in this other forum of doctor practitioners unrelentingly stick to marveling at the apparent effect size in terms of relative risk reduction, our team member who cabled into their discussion focuses on the absolute risk reduction (ARR) to better understand the magnitude of the benefit and draws an analogy with a wildebeest crossing a river, who needs to choose which part of the river bank will be safer in terms of not getting swept away in the current and drowning or not getting eaten by a crocodile! In this particular study it turns out If I were a patient who underwent a PCI along with 5506 patients (or a wildebeest crossing a crocodile filled river with 5506 other wildebeests ) and was randomly assigned to chewing clopidogrel along with 2752 patients (2752 wildebeests crossing from the clopidogrel bank) compared to 2754 patients who continued to gulp aspirin (2754 wildebeests crossing from the aspirin bank), would I be thanking my choice of the clopidogrel river bank when after some time, I would find that 2752-92=2660 patients (wildebeests) like me survived in my clopidogrel group (92 wildebeests got eaten by crocodiles or drowned from the clopidogrel bank) and 2754-128= 2626 people like me survived in the aspirin group (128 wildebeests got eaten or drowned from the aspirin bank)?


The actual result of sharing the above analogy to change collective perception in that group discussion was not gratifying and the discussants stuck to their relative risk eschewing mindset inspite of the above wildebeest rhetoric.


b) Generation of CRH driven Hypothesis for further testing : Current Medical education systems that are far from globally average in many parts of the world work on promoting rote memorization and hardly ever promote critical thinking as a result of which most students once they enter the industry fall prey to simplistic big pharma influences that use relative values driven and interpreted RCT data!" Many of them make it into the middle of the Stanford list of top global researchers because of their meta analysis publications that are a boon to drive big pharma trade! (Ref 25 https://medicinedepartment.blogspot.com/2025/03/udlco-crh-soulful-journal-club-on-oral.html?m=1)




2) Individual PaJR patient centred outcomes (single samples from many case logs available open access):


As a compulsion of mainstream research methodology that values counting and to also take stock of our daily workflow in terms of the number of patients impacted with time, we found that after the editorial invite on 17th February to 17th April our PaJR CBBLE team logged more than 50 PaJR case reports that continues daily without fail (ref 22: https://pajrcasereporter.blogspot.com/?m=1) by our archivist author S with data regularly captured by our corresponding author RB also with the help of other faculty and students in Narketpally as well as PaJR patients and their advocates globally.


The aim was to acquire learning outcomes around each patient requirements that could possibly influence those patient's illness outcomes. The finally logged 50 cases were randomly captured by our corresponding author RB from over two months of 5000 patients encountered by our department in the outpatient and 100 patients that were admitted as inpatients traveling between intensive care to recovery. The corresponding author encounters out patients in the morning from 9:00 AM-12:30 PM and inpatients from 2:30 PM to 3:30 PM daily in Narketpally.


While the above takes stock of a single physician's regular data capturing and processing activity over two months, if we are to provide a bigger longitudinal quantitative estimate from our archives, we may have had to process all the 5000 patients logged and accessible through our case based reasoning dashboard (ref 20 : https://medicinedepartment.blogspot.com/2022/02/?m=1) in 1000 of our students online learning portfolios over last 5 years.


a) As the readings of the three papers was playing heavily on our mind, particularly how causes and effects are connected and how these connections may or may not create context and influence our clinical cognitive decision making pathways, even as we went about doing our daily outpatient and inpatient encounters, we particularly remember an outpatient encountered and shared in our CBBLE on 10th April. This patient was not PaJRed immediately although we did inform them to contact our clinical coordinator and register for PaJR. This was a quick outpatient consultation for a 55 year old woman with anxiety primarily because her daughter was suffering from panic attacks with alkalotic tetany and her daughter was suffering these because she was emotionally attached to another 14 year old girl in her neighbourhood living next door who was suffering from an inoperable brain tumor and doctors had given up on her! We just had to show them this connection based on their own data and reassure them that things will get better with time and asked them to derive further support from us everyday online through their PaJR groups and if possible create a PaJR also for that 14 year old girl who was dying with the brain tumor.


Individual patient centred learning outcomes: Individual patient emotional connections create patient context and hold the key to their solutions


Expected effect on patient's illness outcomes on follow up: Positive


b) Among the inpatient case report logs that were made during this period (ref 21 https://pajrcasereporter.blogspot.com/?m=1), we eventually homed in onto sharing one inpatient here that had a unique Narketpally flavour, one with a hitherto unpublished sign (figure 1)


Accessible at this link: https://medicinedepartment.blogspot.com/2025/03/project-7-years-in-narketpally.html?m=1








Figure 2 shows the patient's head suspended mid air unable to touch the bed and this is our lesser known Narketpally ligamental invisible pillow sign , previously published in non peer reviewed project documents (ref 26: https://medicinedepartment.blogspot.com/2025/03/project-7-years-in-narketpally.html?m=1). This sign is due to ossified posterior longitudinal ligament thought to be caused by endemic fluorosis in Narketpally situated in district Nalgonda as opposed to the much widely published and globally known psychological invisible pillow syndrome due to catatonia( ref 27: https://pmc.ncbi.nlm.nih.gov/articles/PMC11553635/). Figure 1 is also a metaphorical image for the research to practice gap highlighted by Sturmberg et al (1) that we are trying to address with our syndromic approach to medical education and research.


This patient's real illness journey unfolded day by day even as the collective conversational review proceeded in parallel with regular updates of the patient and the causes and effects for his problems getting discussed as embedded parts of his whole lived experience outcomes flowing together into the group's collective cognition and each day brought it's joys, close shaves, frustrations and redemptions for the entire team while the patient lay comatose. (Ref 28 : https://pajrcasereporter.blogspot.com/2025/03/74m-with-neck-pain-hypoglycemic-coma.html?m=1)


One fine day, after 5 days of coma, he suddenly woke up when it was least expected and none of the causes and effects that we thought of in the beginning of his journey with us, which initially suggested a communicable disease, possibly pneumonia and meningitis, appeared to explain his sudden abrupt recovery and we eventually submitted that it was perhaps an atypical viral fever with multiple non communicable disease NCD related comorbidities precipitating heart failure, pulmonary edema and encephalopathy further confounded by his enthesopathy causing his background vertebral ligamental stiffness mimicking meningeal signs with a high CSF protein and low CSF glucose to add to the uncertainty and confusion!


Individual patient centred learning outcomes: Illness complexity can progress to chaos in patient centered medical cognition


Illness outcomes: Yet illness outcomes can be positive inspite of the clinical uncertainty and complete unpredictability in terms of medical cognition.

2nd draft after listing and removing the reference links:

Results:

In brief, divided into thematic learning outcomes as below:

1) Cabled in user-driven learning workflow outcomes from population-based medical

cognition (research fora and journal club-driven CRH and UDLCOs)

Sample a) Wildebeest model of EBM values

Sample b) Hypothesis: Rote memorization-driven big pharma promotion

2) CBBLE (cable) user-driven learning workflow outcomes from individual

patient-centered medical cognition (real PaJR patient-driven CRH and UDLCOs) 

Sample a) Outpatient external medicine connections and outcomes

Sample b) Inpatient internal medicine connections and outcomes

Detailed narrative of the results:

One of the first results of our regular methodological reflections around our work and the three paper authors in the last few months was that, from the themes that emerged, we could divide medical education, research, and practice into two major cognitive flows. Of these, one was driven by population-based researchers, powerful policy makers and managers often working top down high above the forest canopy, efficiently doing things right 1,20 with a few well funded research assistants working for them on ground regularly supplying them controlled experimental data that by design may not need digging deeper into the roots.

The other flow in our collective medical cognition space appears driven by individual

patient physicians, aka doctor practitioners who are largely ground level workers doing

the right thing with a goal toward effectiveness rather than efficiency.

Doctor practitioners regularly and directly interface with patients and build up their

experiential knowledge base one patient at a time. These individual doctor patient

encounter experiences are largely captured in individual memories and if expressed

directly from memory would be rightly labeled anecdotal although sometimes some of these doctors also share their real patient data captured as clinical case reports logically lowest in the hierarchy that consume very low resources (analogous to phase 1 and phase 4 in clinical trial parlance) in comparison to population based medicine researchers who are higher above in the canopy and have more structured high resource tools to set up well randomized experiments (phase 2 and phase 3 clinical trials), unbiased by ground truths and well designed to control for real life confounders scattered chaotically on the ground.

Most doctors are also trained in population-based medical cognition through current

medical education and research training systems, and our group, too, continues to

regularly dabble in it using medical cognition tools created with the flow, such as critical realist heutagogy 14,15. Population-based medicine learning exercises by our group are often done through journal clubs around published randomised controlled trials that we discuss in various online groups we are cabled in. For a larger part of our day, we continue to engage in self-directed learning around our individual real patient practice, and hence, the results of our regular workflow exercise are divided into thematic learning outcomes as below:

1) Cabled in user-driven learning workflow outcomes from population-based medical

cognition (research fora and journal club-driven CRH and UDLCOs) 

2) CBBLE (cable) user-driven learning workflow outcomes from individual

patient-centered medical cognition (real PaJR patient-driven CRH and UDLCOs)

The Influence of the article and its two commentaries in shaping our learning outcomes and CRH-driven UDLCOs during the time taken to write this paper is also factored into the above. We haven't counted the number of CRH-driven UDLCOs we created during these few months, but they are available open access for both quantitative and qualitative analysis in our archived online learning portfolios 19, 21, 22.

1) Population-based research phase 2-3 clinical trials journal club learning outcomes:

a) The Wildebeest model of EBM values was inspired by the commentary on EBM

values! 2,23

The most difficult aspect of the EBM definition, which states that it "combines the best available evidence with clinical expertise and patient values to provide the best care for the individual patient," is the part about how to combine patient values. The current population-centred average outcomes research paradigm is focused on values that have been described below:

One of our CBBLE (cable) conversations logged on April 3 revolves around a journal

club discussion on a recent study comparing the efficacy of clopidogrel versus aspirin

monotherapy in high-risk patients post-PCI (percutaneous coronary intervention)24.

While the original posters in this other forum of doctor practitioners unrelentingly stick to marveling at the apparent effect size in terms of relative risk reduction, our team

member who cabled into their discussion focuses on the absolute risk reduction (ARR)

to better understand the magnitude of the benefit and draws an analogy with a

wildebeest crossing a river, who needs to choose which part of the river bank will be

safer in terms of not getting swept away in the current and drowning or not getting eaten by a crocodile! In this particular study it turns out If I were a patient who underwent a PCI along with 5506 patients (or a wildebeest crossing a crocodile filled river with 5506 other wildebeests ) and was randomly assigned to chewing clopidogrel along with 2752 patients (2752 wildebeests crossing from the clopidogrel bank) compared to 2754 patients who continued to gulp aspirin (2754 wildebeests crossing from the aspirin bank), would I be thanking my choice of the clopidogrel river bank when after some time, I would find that 2752-92=2660 patients (wildebeests) like me survived in my clopidogrel group (92 wildebeests got eaten by crocodiles or drowned from the clopidogrel bank) and 2754-128= 2626 people like me survived in the aspirin group (128 wildebeests got eaten or drowned from the aspirin bank)?

The actual result of sharing the above analogy to change collective perception in that group discussion was not gratifying and the discussants stuck to their relative risk

eschewing mindset inspite of the above wildebeest rhetoric.

b) Generation of CRH driven Hypothesis for further testing: Current Medical education

systems that are far from globally average in many parts of the world work on promoting

rote memorization and hardly ever promote critical thinking as a result of which most

students once they enter the industry fall prey to simplistic big pharma influences that

use relative values driven and interpreted RCT data!" Many of them make it into the

middle of the Stanford list of top global researchers because of their meta-analysis

publications that are a boon to drive big pharma trade!25

2) Individual PaJR patient-centred outcomes (single samples from many case logs

available open access):

As a compulsion of mainstream research methodology that values counting and to also take stock of our daily workflow in terms of the number of patients impacted with time, we found that after the editorial invite on 17th February to 17th April our PaJR CBBLE team logged more than 50 PaJR case reports that continues daily without fail 22 by our archivist author S with data regularly captured by our corresponding author RB also with the help of other faculty and students in Narketpally as well as PaJR patients and their advocates globally.

The aim was to acquire learning outcomes around each patient's requirements that

could possibly influence those patients' illness outcomes. The final logged 50 cases

were randomly captured by our corresponding author RB from over two months of 5000 patients encountered by our department in the outpatient and 100 patients that were admitted as inpatients traveling between intensive care to recovery. The corresponding author encounters out patients in the morning from 9:00 AM-12:30 PM and inpatients from 2:30 PM to 3:30 PM daily in Narketpally. While the above takes stock of a single physician's regular data capturing and processing activity over two months, if we are to provide a bigger longitudinal quantitative estimate from our archives, we may have had to process all the 5000 patients logged and accessible through our case based reasoning dashboard 19

in 1000 of our students online learning portfolios over last 5 years.

As the readings of the three papers was playing heavily on our mind, particularly how causes and effects are connected and how these connections may or may not create context and influence our clinical cognitive decision making pathways, even as we went about doing our daily outpatient and inpatient encounters, we particularly remember an outpatient encountered and shared in our CBBLE on 10th April. This patient was not PaJRed immediately, although we did inform them to contact our clinical coordinator and register for PaJR. 

This was a quick outpatient consultation for a 55 year old woman with anxiety primarily because her daughter was suffering from panic attacks with alkalotic tetany and her daughter was suffering these because she was emotionally attached to another 14 year old girl in her neighbourhood living next door who was suffering from an inoperable brain tumor and doctors had given up on her! We just had to show them this connection based on their own data and reassure them that things will get better with time, and asked them to derive further support from us everyday online through their PaJR groups and if possible create a PaJR also for that 14 year old girl who was dying with the brain tumor.

Individual patient-centred learning outcomes: Individual patient emotional

connections create patient context and hold the key to their solutions

Expected effect on patient's illness outcomes on follow-up: Positive

b) Among the inpatient case report logs that were made during this period 21, we

eventually homed in on sharing one inpatient here that had a unique Narketpally flavour, one with a hitherto unpublished sign (figure 2), and is accessible here 26

Figure 2 shows the patient's head suspended mid-air, unable to touch the bed, and this

is our lesser-known Narketpally ligamental invisible pillow sign, previously published in

non-peer-reviewed project documents 26

This sign is due to an ossified posterior

longitudinal ligament, thought to be caused by endemic fluorosis in Narketpally, situated

in the district Nalgonda, as opposed to the much widely published and globally known

psychological invisible pillow syndrome due to catatonia 27

Figure 2 is also a metaphorical image for the research-to-practice gap highlighted by Sturmberg et al 1 that we are trying to address with our syndromic approach to medical education and

research.

This patient's real illness journey unfolded day by day even as the collective conversational review proceeded in parallel with regular updates of the patient and the

causes and effects for his problems getting discussed as embedded parts of his whole

lived experience outcomes flowing together into the group's collective cognition and

each day brought it's joys, close shaves, frustrations and redemptions for the entire

team while the patient lay comatose 28

One fine day, after 5 days of coma, he suddenly woke up when it was least expected and none of the causes and effects that we thought of in the beginning of his journey with us, which initially suggested a communicable disease, possibly pneumonia and meningitis, appeared to explain his sudden abrupt recovery and we eventually

submitted that it was perhaps an atypical viral fever with multiple non communicable

disease NCD related comorbidities precipitating heart failure, pulmonary edema and encephalopathy further confounded by his enthesopathy causing his background vertebral ligamental stiffness mimicking meningeal signs with a high CSF protein and low CSF glucose to add to the uncertainty and confusion!

Individual patient-centred learning outcomes: Illness complexity can progress to

chaos in patient-centered medical cognition

Illness outcomes: Yet illness outcomes can be positive inspite of the clinical

uncertainty and complete unpredictability in terms of medical cognition.

Discussion:


What then is Narketpally syndrome?


The first thing that hit the corresponding author about Narketpally as he walked into it's hospital corridors for the first time in August 19 , 2017 as per the entry in his online learning portfolio (ref 26 : https://medicinedepartment.blogspot.com/2025/03/project-7-years-in-narketpally.html?m=1)
is the high number of hospital patients walking with sticks!


From a population based medicine perspective, Narketpally syndrome may have been a term applied to patients suffering from certain events such as skeletal abnormalities manifesting as ossified posterior longitudinal ligament causing the hitherto undescribed ligamental invisible pillow sign as opposed to the previously described psychological invisible pillow sign in catatonic schizophrenia. These patients also have features suggestive of osteoarthritis, spondyloarthropathy as well as dental enamel affection.


(Ref 29: BMJ 1955 paper from Osmania: https://pmc.ncbi.nlm.nih.gov/articles/PMC1981710/?page=1)


However this epidemiologic population based syndromic approach may have been applied when the first cases were discovered here in 1937 and subsequently reported in 1955 (ref 29) but nearly 100 years later, it's no longer a syndrome but more of an established disease with a single epidemiological cause and definitive pattern of effect.
When a syndrome with multiple causes and effects, reflecting a high degree of uncertainty is specifically identified with a definite cause and effect, this becomes a disease which is attributed a greater degree of certainty by current medical cognition.(Ref 30: Calvo, F; Karras, BT; Phillips, R; Kimball, AM; Wolf, F (2003). "Diagnoses, Syndromes, and Diseases: A Knowledge Representation Problem". AMIA Annu Symp Proc. 2003: 802.


While a syndromic approach is often used to tackle multiple causes and effects intertwined in diagnostic and therapeutic uncertainty giving rise to clinical complexity, we know from an above mentioned etymology of syndrome that it simply means "together we flow" and hence moving away from current traditional population based medicine syndromic approaches, Narketpally syndrome describes a unique medical education and research syndrome that promotes global patient centered learning toward local caring and one of it's current local epicenter is at Narketpally. Global learning toward local caring is a syndromic approach to medical education and research for the sake of each of it's primary beneficiaries (patients) and this syndrome possibly already exists in many geo locations while some of us from Narketpally are writing about it here.


While Narketpally syndrome may appear both literal and metaphorical, it is a space of 24x7 online learning, blended with captured offline data processed by Web 2.0-3.0 shared online cognition and this space has been described before as CBBLE (pronounced cable) broadly, case based blended learning ecosystem (ref 10:
https://pmc.ncbi.nlm.nih.gov/articles/PMC6163835/).


Past of Narketpally syndrome: an evolutionary timeline to current technique (and rhetoric):


To borrow a quote from a summary of complexity in Sumpter's taxonomy elucidated in the commentary by Greenhalgh (ref:2)


"Complexity thinking considers the system as a whole and the dynamics of its evolution. The system evolves as relationships between nodes in the network shift. The individual is nested in a wider context and influenced by both historical and present‐day connections. As with chaos thinking, a standard input will not generate a specific output. You cannot step into the same river twice." Unquote: Notice that we are back to the river analogy here again!


AI means asynchronous intelligence that thrives in a two dimensional academic flat land:


Let's explore further in a thought experiment about what would education and research mean to cave men. It could mean another tool to solve their day to day issues such as trying to asynchronously communicate through a sketch on their cave walls where they had been hunting and where they would be leaving for the next hunt so that the next batch of hunting cave men who would be arriving at that cave a few days later could track their whereabouts? As cavemen evolved into us, the problems embedded in the whole ecosystem changed while human ability to learn through collective cognition didn't but became more refined. (Ref 31 https://medicinedepartment.blogspot.com/2021/06/draft-3a-scholarship-of-integration-and.html?m=1)
Rhetoric: Even now as humans continue to flow through time along with others who share the same space time globally, they and their uncertainties are like particles suspended in river water, which has a way of flowing around any object in it's path, a unique characteristic of the river that allows all other smaller particles in it to continue to flow unhindered albeit with a high degree of uncertainty of what future brings.


Human animals invented AI beginning with asynchronous intelligence through their ability to use cave painting tech to convert multidimensional real life data into two dimensional data in an xy axis cave wall that later evolved to paper and electronic media so that they could eventually manage their lives better as artistic modelling was easier in a two dimensional virtual plane than a multi dimensional real plane!


We have come all the way from primordial AI (aka asynchronous intelligence) to modern AI that models primordial AI to produce some currently interesting results particularly if the data capture is asynchronously hyperlocal.

Most learning is a process of objectively structuring subjective complex multidimensional real life data (blooms level 3-5) into a two dimensional space (blooms level 1 aka knowledge) that can be stored forwarded asynchronously and modeled conceptually to gain understanding (blooms level 2) through further analysis ( level 4) and evaluation (level 5) and then relooped into the learning ecosystem as creative communication/publication (level 6). This learning is cyclic and one can keep moving in and out of these levels at any entry or exit point regardless of level numbers.

Ref 32 :https://medicinedepartment.blogspot.com/2023/12/ongoing-project-draft-optimizing.html?m=1
We have previously shared our current hyperlocal workflow samples and reference them here:


33 Telangana:


https://userdrivenhealthcare.blogspot.com/2024/02/pajr-telugu-guidelines-for-sharing.html?m=1


34 Bengal:

https://medicinedepartment.blogspot.com/2023/12/pajr-daily-activities-energy-outputs.html?m=1


35 Madhya Pradesh:


https://kg83797.blogspot.com/2017/04/a-24-years-old-man-with-left-hand.html?m=1


https://udhc155.blogspot.com/2016/04/alangium460salvifolium02a20-year-old.html?m=1

(ref 36: https://medicinedepartment.blogspot.com/2024/11/technology-end-user-driven-ecosystem.html?m=1)


Our first publication in the same journal here gave us a platform to voice our thoughts around dilemmas concerning the lack of contextual connections in randomized controlled trials and our inability to often find them meaningful to our individual patients. (Ref 18: process of evidence-based medicine and the search for meaning https://pubmed.ncbi.nlm.nih.gov/17683292/)
We also became acutely aware of the gap between two types of physicians. There were those that only looked after a population and were comfortable with current epidemiological (forest canopy) approaches to healthcare essentially doing things right as far as management efficiency is concerned (Ref 1 Sturmberg, ref 19 Druckerhttps://www.saudercpa.com/blog/2024/02/10/peter-druckers-timeless-management-wisdom-ten-quotes-to-lead-and-inspire/#:~:text=Drucker%20(%20Peter%20Drucker%20)%20's%20distinction,about%20setting%20the%20right%20direction%20and%20priorities.)


Then there were those that cared for individual patients and were grounded with roots of every individual trees they touched on. These physicians appeared to have a felt need to somehow connect the roots of individual internal medicine and it's working mechanisms. They continue to connect those with external medicine branches of the forest canopy and beyond that. And again as emphasized by Sturmberg from Drucker this was their way of doing the right thing as in effectiveness that provides unique leadership to encompass our entire whole learning and practice ecosystem. (Ref 1, ref19 https://www.saudercpa.com/blog/2024/02/10/peter-druckers-timeless-management-wisdom-ten-quotes-to-lead-and-inspire/#:~:text=Drucker%20(%20Peter%20Drucker%20)%20's%20distinction,about%20setting%20the%20right%20direction%20and%20priorities)




Present whole systems complexity and it's uncertain parts:


"Uncertainty about the diagnosis or the presence of a syndrome has potential advantages. It may lead the clinician to do more patient assessment, to review available literature, and to consult other clinicians. Finally, it should encourage the clinician to have a more detailed discussion with the patient regarding the current status of the diagnosis, diagnostic options, and treatment options." (Ref 31: When should clinicians use the term syndrome?
The American Journal of the Medical Sciences Volume 365, Issue 6, June 2023, Pages 475-479 https://www.sciencedirect.com/science/article/abs/pii/S0002962923010601)


Now if we look at the above statement in the context of "Every problem is embedded in a greater whole," very intriguingly, when we see how Sturmberg and Mercuri's paper urges authors to rethink research models from a strict “cause-and-effect” base to that of the intricate interplay of multiple “ causes-and-effects,” the meaning of the word 'syndrome' as in it's original indo-greco-latin-european roots and it's modern medical interpretation appears to flow together in an integrative whole and exhorts our readers to think of illness journeys not in terms of simple single cause and effects but recognise the fact that each and every individual illness journey is a greater whole embedded with smaller but connected causes and effects constituting it's problem parts! (Rhetoric)


Current Hillary steps toward the pinnacle of Medical Cognition from Web 0.0 to Web 3.0:


Much has happened in the 7 years since we initiated the syndromic CBBLE global learning toward local caring in Narketpally with further evolution of critical realist heutagogy embracing critical realism and heutagogy that allows healthcare learners to 1) understand complex outcomes, (2) optimize interventions, and (3) research biopsychosocial pathways. Such questions are central to evidence-based practice, chronic disease management, and population health.


Ref 32: https://en.m.wikipedia.org/wiki/Critical_realism_(philosophy_of_the_social_sciences)



Ref 12 : https://pubmed.ncbi.nlm.nih.gov/37335625/












Figure 3


There is a very useful figure that illustrates critical realist view of stratified reality shared by Hoddy et al (ref 33,34), Ref 33 : Source: https://www.researchgate.net/publication/342246458_Critical_realism_and_constructivism_merging_research_paradigms_for_a_deeper_qualitative_study/figures?lo=1
Ref 34 Full text: https://fenix.iseg.ulisboa.pt/downloadFile/281608120804971/Critical%20realism%20and%20GT%20Hoddy%202019.pdf,
to which we have added our own diagrammatic modification from a medical cognition lens (Figure 2) and the essence of it is all about the individual patient's and physician's cognition at the centre of the concentric circles corresponding to Hoddy's domain of the 'empirical' as in perceptions, experiences and observations of 'individual cognition' while the surrounding concentric circle forms a ring of 'actual collective cognition' that tries to tie together all knowledge elements into an 'ontology' many of which are unknown and form the connecting link to the next ring which is Hoddy's domain of the 'real' that contains all the structures and mechanisms that generate events that again feed back cyclically into the central concentric circle of individual perception of empirical events surrounded by the ring of collective cognitive interpretation of 'actual' events.


In our CBBLE and PaJR groups we witness daily in real time, a co creation of user driven learning community ontologies powered by Web 2.0 critical realist heutagogy through team based learning between multiple stakeholders such as health professional learners, faculty, patients and their relatives that is increasingly supported by Web 3.0 technology where all these stakeholders co create as online system users, with a username, something that has been labelled in the past user driven healthcare.


Ref 35: https://pubmed.ncbi.nlm.nih.gov/19018905/ and Ref 36: Biswas, R. & Martin, C. M. (Eds.). (2011). User-Driven Healthcare and Narrative Medicine: Utilizing Collaborative Social Networks and Technologies. IGI Global. https://doi.org/10.4018/978-1-60960-097-6
https://www.igi-global.com/book/user-driven-healthcare-narrative-medicine/41908#:~:text=User%2DDriven%20Healthcare%20and%20Narrative%20Medicine:%20Utilizing%20Collaborative,problems%20in%20healthcare%2C%20utilizing%20real%20life%20examples.


This co creation has enabled healthcare educator practitioners to not only remain focused on real patient care centred learning outcomes but develop realist ontologies for medical education and practice such as "real patient OSCEs" that retain whole patient clinical complexity rather than reduce it to smaller parts in order to optimize clinical complexity.


Ref 36 https://medicinedepartment.blogspot.com/2024/01/medical-cognition-cpd-jan-25-2024theme.html?m=1


With recent changes in the school curriculum, there may be no need to again educate Medical Professionals about AI basics as they will come well prepared from schools, but to answer the real question about who/what will train the trainers at such a scale so fast, the logical answer from all that we have shared above appears to be: who else but Web 3.0 driven AI itself combined with Web 2.0 human looped critical realist heutagogy?


There are two key elements to the Critical Realist Heutagogic Approach**:


Self-Directed Learning (Heutagogy): Encourages learners to actively question, analyze, and synthesize information rather than passively accepting claims. This aligns with higher-order Bloom’s Taxonomy skills (analysis, evaluation, creation) over rote memorization.


Critical Realism: Focuses on uncovering underlying mechanisms and structural biases.


Ref 37 https://medicinedepartment.blogspot.com/2024/10/critical-realist-pedagogy-and-theatre.html?m=1

Present failure:


"Many if not most students come to the university in order to gain access to an eventual enfranchisement in 'the establishment,' not to critique and reject its privileges."



Students want to gain, rather than to critique, positions of privilege, as encouraged by critical pedagogues."



Ref 38 https://en.m.wikipedia.org/wiki/Critical_pedagogy



Hence out of 1000 students in the five batches of 200 each in our Narketpally medical college campus we can only hope to have 1-2 from each batch and mostly they don't reach us till their final years so we have just 1-2 out of 1000 to help us along with on an average 1 resident in a batch of 10 who may be able to develop self directed learning skills as a first step to critical realist heutagogy. As a result the daily Web 2.0 driven team based learnings in our groups comprise of patients, student alumni from those 1-2 per batch and occasional current residents who are sometimes forced into those groups from a service angle toward data capture and informational continuity.

In our bid to train some of our students to use critical realist heutagogy in a patient centred manner we have tried to slowly make them graduate from an individual patient level to the system level to study the trajectory of a *disease* or a *phenotype* so that it promotes *proactive healthcare* instead of *reactive healthcare.* This aims to reduce patient harm and conserve expensive resources.(anecdotal rhetoric about what is best described as a minimalist but currently functioning prototype).


We look at PaJR patient data as deep phenotypes and chart their subsequent trajectories through informational data continuity toward pattern recognition for prediction.


"Deep phenotyping represents a comprehensive approach to understand disease phenotypes by integrating detailed data from electronic health records (EHRs), clinical notes, and high-throughput technologies. Recent advancements in artificial intelligence (AI) and machine learning have significantly enhanced our ability to analyze and interpret complex phenotypic data, making it possible to uncover previously hidden patterns and correlations. Techniques such as natural language processing (NLP), deep learning, and network analysis are increasingly being used to extract and structure phenotypic information from unstructured data sources. These developments are particularly exciting as they pave the way for more precise disease classification and personalized treatment strategies." (Ref 38 https://link.springer.com/collections/ghejiifdif


When we encounter a single patient and assess her past trajectory, we also have similar past patient data where we have the entire completed trajectory and we then try to match the current patient incomplete trajectory to see which patient scenario it fits with closest at the stage where she has sought help and that can proactively reduce harms suffered by the previously documented trajectories where we can take better informed decisions to avoid certain pathways.


These can be done through DL ML AI driven 'case based reasoning' CBR engines and to quote:


"CBR solves problems by using or adjusting solutions to old problems. It is a problem-solving paradigm that is fundamentally different from other major AI approaches. Instead of relying solely on the general knowledge of a problem domain or making associations based on generalized relationships between problem descriptors and conclusions, CBR can use the specific knowledge of specific problem situations (cases) experienced in the past to solve similar, new problems. A second important difference is that CBR is an incremental, continuous learning method that retains new experiences in solving each problem and then applies it to solve new problems in the future." Ref 39: https://www.mdpi.com/2076-3417/14/16/7130


Currently using a syndromic approach to individual patient centered medical education and research, we match these patient trajectories partly manually and partly automatically through qualitative thematic analysis using current generation AI LLM driven thematic analysis tools.


For example if our currently encountered patient was one of the patients out of these 50 patients of anemia that we have archived from our EMR summaries, then one could extract the themes from each patient: Ref 40: https://medicinedepartment.blogspot.com/2024/07/y24narketpally50n-clinical-complexity.html?m=1


and check which of those matched our current patient.


Similarly for altered sensorium here: Ref 41 https://medicinedepartment.blogspot.com/2024/07/meta-ai-driven-qualitative-thematic.html?m=1


And cardio renal failure here: Ref 42 https://medicinedepartment.blogspot.com/2024/07/y24narketpally50n-cardio-renal-failure.html?m=1


And even for something as common place for trunkal obesity here: Ref 43 https://medicinedepartment.blogspot.com/2024/09/thematic-analysis-of-biopsychosocial.html?m=1


However it's still a work in progress with a lot of non peer reviewed data building up through primary https://pajr.in/ EMR data which for the first time can be well analysed by LLMs once we figure out a way to further automate this process seamlessly between our primary patient Ux and the data processing Ux.


Future integrative resolution of current differences in the world views of Sturmberg's pragmatic , effective, individual person centred , contextual , connected, syndromic, doing the right thing approach vs Ioannidis's population based, single cause and effect, no nonsense, doing things right approach vs Greenhalgh's non mathematical value driven approach:


Quoting from Section 5.3 of Sturmberg's paper "The embrace of the pragmatic trial" aiming to show the variable (real world) effects of medical interventions should be understood as a methodological approach to pattern identification.


Each of the outcome patterns can then be further explored to identify the potential biological and environmental variables that contributed to ‘creating’ this pattern. The next step will involve an exploration of the dynamic patterns (pathways) between the variables of each pattern. Having identified the variables involved allows a specific focus on studying potential ‘causal’ pathways, including any variability between patients in the same cluster." (Ref 1)


Sturmberg et al stress on two key constructs to make this possible - (i) dynamic modeling to find causal pathways between variables interacting over time, and (ii) complexity theory - discovering emergent behaviour, feedback loops et al. Figure 7 in their paper is a key diagram that explains this.


Most data analysis stops at the stage of clustering or finding relative significance of variables and what the authors appear to propose is to further do dynamic modeling on these variables over time to find causal pathways. The authors hope that complexity theory when applied to this, will result in new treatment protocols in medicine.


Machine learning already has tools to do this, however we haven't seen any paper or project that uses all these tools together in the way the author explains in Figure 7.


Few examples of tools include (i) modeling dynamic Bayesian networks with tools such as pgmpy https://github.com/pgmpy/pgmpy, and (ii) simulating multi-agent systems to observe emergent behavior and feedback loops based on complexity theory, with tools such as Mesa (https://github.com/projectmesa/mesa) or AgentTorch (https://github.com/AgentTorch/AgentTorch)


Applying this to our work - the CBBLE/PaJR process is a functional low resource settings prototype of a pragmatic trial - understanding patients with a 360 view, conducting treatment with that 360 context, collaborative treatment and learning process and consented documentation as PaJR case report EHRs available open access and logged daily in online learning portfolios archived here (ref PaJR dashboard).Very few health systems follow this construct. We see a huge opportunity to follow the data analysis process mentioned in Sturmberg's paper and see if it indeed points to patterns that haven't been seen before or helps evolves new protocols not tried before. What is needed is well formed input to current AI processing systems. For example for creating dynamic bayesian networks, or temporal graphs, we need the complete context of a patient broken down by parameters, the cause/intervention and then the state of the patient again in the same or more parameters. This parameterization is the biggest challenge and will need help of LLMs and doctors. Dr Van Der Shaar's work in Cambridge around TemporAI (https://github.com/vanderschaarlab/temporai) for healthcare data is also in the same direction, although not at the level of what the paper is proposing.


Future of an albeit Orwellian, transparent, accountable, syndromic approach to medical education and research and it's ethical ramifications:


Medical professionals must prioritize afore mentioned case based reasoning CBR, healthcare information systems that regularly provide a transparent individual patient clinical audit in real time with rigorous evidence appraisal to avoid therapeutic missteps. (Ref 44: https://pmc.ncbi.nlm.nih.gov/articles/PMC4535121/)


Transparency in Research would not just remain confined to highlighting the importance of publishing all trial data, including negative results but would insist on all making available contextual data surrounding each and every trial patient participant to inform clinical decisions.


Lessons for Medical Education:


Curriculum Shift: An individual patient centred syndromic approach will enable moving beyond foundational knowledge (Bloom’s levels 1–2) to cultivate critical appraisal skills and teach students to dissect study designs, recognize bias, and assess real-world applicability.


Case-Based Learning: Using regular, connected and contextual "real patient" data over simulations can better demonstrate consequences of uncritical acceptance of biased data and this


Advocacy Training: Can empower future clinicians and their patients to challenge misinformation and advocate for ethical research practices.

Key phrases and sentences as diagnostic markers (instead of major and minor criteria) for a unique medical education research syndrome: 

Syndrome as in it's etymology (Greek Sun Drammein, Sanskrit Sama Druma, together we flow), 

Collective medical cognition that thrives in connected contexts between individual patients and physicians flowing in and out of their lives like a river, 

Tools of collective medical cognition in Patient journey record PaJR, 

Case based blended learning ecosystem CBBLE (cable), 

User driven healthcare, UDHC, 

Critical realist heutagogy CRH, 

User driven learning community ontologies UDLCOs and 

AI driven case based reasoning CBR engines, 

Tree canopy (population based medicine, phase 2-3 clinical trials) stem connected to grounded tree roots (individual patient centered medicine, phase 1-4 trials), 

Translating positive learning outcomes to positive illness outcomes, 

Being together in the flow regularly with our patients and their journeys, 

Non local Quantum entangled theory of everything aka Everything is connected, 

Human ontologies as life to death trajectories connected through UDLCOs, 

Cyclical evolution and dissolution 

of humans and other beings

Whole-o-graphic systems where every fractal or factal broken through reductionism still shows the whole 

Those who know the whole actually don't and those who don't know it actually do (Kena upanishad and it's subsequent variations from Confucius to critical realism) 

Ref 57: https://www.wisdomlib.org/hinduism/book/kena-upanishad-shankara-bhashya/d/doc145048.html

Research is to become a witness to truth, a stream/river of consciousness flooding our collective cognition and one can only perceive it if one can gather compassion and humility from everyone's life journeys!

Conclusion: 

Borrowing from the learnings from Sturmberg's (1), Greenhalgh's (2), Ioannidis (3) and our previous work, this current paper proposes a new design that will be able to:
tie up every data element in an ontology framework driven by case based reasoning engines and make them flow together in a future AI supported whole-o-graphic ecosystem where every part is embedded in the whole such that when one breaks off different fragments from the whole they find the whole still contained in the individual broken fragments!


The above rhetoric is tied to the populist imagination of holography in current human collective cognition and isn't scientifically impossible or untrue (ref 44 : https://en.m.wikipedia.org/wiki/Holography#:~:text=When%20a%20hologram%20is%20cut,to%20the%20scene%20it%20recorded and while case based reasoning isn't part of the current populist rhetoric, it's rapidly growing pace especially with deep machine learning techniques (ref 45:https://pmc.ncbi.nlm.nih.gov/articles/PMC544898/

Our paper urges a shift in the design of medical learning and research—from merely trusting authority to embracing critical inquiry and yet maintain a togetherness in our collective cognitive flow concordant with ancient etymological definitions of 'syndrome.' By integrating heutagogic principles and critical realism into individual patient centred contextual inquiries, educators and researchers can better navigate complex, biased information landscapes, ultimately improving patient care through evidence-based, ethically informed decisions.

2nd draft after listing and removing the reference links:

Discussion:

What then is Narketpally syndrome?

The first thing that hit the corresponding author about Narketpally as he walked into itshospital corridors for the first time on August 19, 2017, as per the entry in his online learning portfolio 26, is the high number of hospital patients walking with sticks!

From a population-based medicine perspective, Narketpally syndrome may have been a term applied to patients suffering from certain events such as skeletal abnormalities manifesting as ossified posterior longitudinal ligament, causing the hitherto undescribed

ligamental invisible pillow sign as opposed to the previously described psychological

invisible pillow sign in catatonic schizophrenia. These patients also have features suggestive of osteoarthritis, spondyloarthropathy, as well as dental enamel affection 29

.

However, this epidemiologic population-based syndromic approach may have been

applied when the first cases were discovered here in 1937 and subsequently reported in

195529, but nearly 100 years later, it's no longer a syndrome but more of an established disease with a single epidemiological cause and definitive pattern of effect. 

When a syndrome with multiple causes and effects, reflecting a high degree of

uncertainty, is specifically identified with a definite cause and effect, this becomes a

disease that is attributed a greater degree of certainty by current medical cognition 30

.

While a syndromic approach is often used to tackle multiple causes and effects

intertwined in diagnostic and therapeutic uncertainty giving rise to clinical complexity, we know from an above mentioned etymology of syndrome that it simply means "together we flow" and hence moving away from current traditional population based medicine syndromic approaches, Narketpally syndrome describes a unique medical education and research syndrome that promotes global patient centered learning toward local caring and one of it's current local epicenter is at Narketpally. 

Global learning toward local caring is a syndromic approach to medical education and research for the sake of each of its primary beneficiaries (patients), and this syndrome possibly already exists in

many geo locations, while some of us from Narketpally are writing about it here.

While Narketpally syndrome may appear both literal and metaphorical, it is a space of

24/7 online learning blended with captured offline data processed by Web 2.0-3.0

shared online cognition. This space has been described before as a CBBLE (pronounced cable) broadly, a case-based blended learning ecosystem 30

.

Past of Narketpally syndrome: an evolutionary timeline to current technique (and rhetoric):

To borrow a quote from a summary of complexity in Sumpter's taxonomy elucidated in the commentary by Greenhalgh 2

.

"Complexity thinking considers the system as a whole and the dynamics of its evolution.

The system evolves as relationships between nodes in the network shift. The individual

is nested in a wider context and influenced by both historical and present‐day

connections. As with chaos thinking, a standard input will not generate a specific output. You cannot step into the same river twice." Unquote: Notice that we are back to the river analogy here again!

AI means asynchronous intelligence that thrives in a two-dimensional academic flatland:

Let's explore further in a thought experiment about what education and research mean to cave men. It could mean another tool to solve their day to day issues such as trying to asynchronously communicate through a sketch on their cave walls where they had been hunting and where they would be leaving for the next hunt so that the next batch of hunting cave men who would be arriving at that cave a few days later could track their whereabouts? As cavemen evolved into us, the problems embedded in the whole

ecosystem changed while human ability to learn through collective cognition didn't, but

became more refined 32

.

Rhetoric: Even now as humans continue to flow through time along with others who

share the same space time globally, they and their uncertainties are like particles

suspended in river water, which has a way of flowing around any object in it's path, a

unique characteristic of the river that allows all other smaller particles in it to continue to

flow unhindered albeit with a high degree of uncertainty of what future brings.

Human animals invented AI beginning with asynchronous intelligence through their

ability to use cave painting tech to convert multidimensional real life data into two

dimensional data in an xy axis cave wall that later evolved to paper and electronic

media so that they could eventually manage their lives better as artistic modelling was

easier in a two dimensional virtual plane than a multi dimensional real plane!

We have come all the way from primordial AI (aka asynchronous intelligence) to modern AI that models primordial AI to produce some currently interesting results, particularly if the data capture is asynchronously hyperlocal.

Most learning is a process of objectively structuring subjective complex

multidimensional real life data (blooms level 3-5) into a two dimensional space (blooms

level 1 aka knowledge) that can be stored forwarded asynchronously and modeled

conceptually to gain understanding (blooms level 2) through further analysis ( level 4)

and evaluation (level 5) and then re-looped into the learning ecosystem as creative

communication/publication (level 6). This learning is cyclic, and one can keep moving in and out of these levels at any entry or exit point, regardless of level numbers 32

We have previously shared our current hyperlocal workflow samples and referenced them here for Telangana 33

, Bengal 34 and Madhya Pradesh35,36,37.

Our first publication in the same journal here gave us a platform to voice our thoughts around dilemmas concerning the lack of contextual connections in randomized controlled trials and our inability to often find them meaningful to our individual patients 38

We also became acutely aware of the gap between the two types of physicians. There were those who only looked after a population and were comfortable with current epidemiological (forest canopy) approaches to healthcare, essentially doing

things right as far as management efficiency is concerned 1,20

.

Then there were those who cared for individual patients and were grounded with the roots of every individual tree they touched. These physicians appeared to have a felt need to somehow connect the roots of individual internal medicine and its working

mechanisms. They continue to connect those with external medicine branches of the

forest canopy and beyond that. And again, as emphasized by Sturmberg from Drucker,

this was their way of doing the right thing, as in effectiveness that provides unique

leadership to encompass our entire whole learning and practice ecosystem1,20

.

Present the whole system's complexity and its uncertain parts:

"Uncertainty about the diagnosis or the presence of a syndrome has potential

advantages. It may lead the clinician to do more patient assessment, to review available

literature, and to consult other clinicians. Finally, it should encourage the clinician to

have a more detailed discussion with the patient regarding the current status of the

diagnosis, diagnostic options, and treatment options."39

Now if we look at the above statement in the context of "Every problem is embedded in

a greater whole," very intriguingly, when we see how Sturmberg and Mercuri's paper

urges authors to rethink research models from a strict “cause-and-effect” base to that of the intricate interplay of multiple “ causes-and-effects,” the meaning of the word

'syndrome' as in it's original indo-greco-latin-european roots and it's modern medical

interpretation appears to flow together in an integrative whole and exhorts our readers

to think of illness journeys not in terms of simple single cause and effects but recognise

the fact that each and every individual illness journey is a greater whole embedded with smaller but connected causes and effects constituting it's problem parts! (Rhetoric)

Current Hillary steps toward the pinnacle of Medical Cognition from Web 0.0 to Web 3.0:

Much has happened in the 7 years since we initiated the syndromic CBBLE global

learning toward local caring in Narketpally with further evolution of critical realist

heutagogy embracing critical realism and heutagogy that allows healthcare learners to

1) understand complex outcomes, (2) optimize interventions, and (3) research

biopsychosocial pathways. Such questions are central to evidence-based practice,

chronic disease management, and population health9,40

.

There is a very useful figure that illustrates the critical realist view of stratified reality

shared by Hoddy et al.41,42 to which we have added our own diagrammatic modification from a medical cognition lens (Figure 2) and the essence of it is all about the individual patient's and physician's cognition at the centre of the concentric circles corresponding to Hoddy's domain of the 'empirical' as in perceptions, experiences and observations of 'individual cognition' while the surrounding concentric circle forms a ring of 'actual collective cognition' that tries to tie together all knowledge elements into an 'ontology' many of which are unknown and form the connecting link to the next ring which is Hoddy's domain of the 'real' that contains all the structures and mechanisms that generate events that again feed back cyclically into the central concentric circle of individual perception of empirical events surrounded by the ring of collective cognitive interpretation of 'actual' events.

In our CBBLE and PaJR groups we witness daily in real time, a co creation of user

driven learning community ontologies powered by Web 2.0 critical realist heutagogy through team based learning between multiple stakeholders such as health professional learners, faculty, patients and their relatives that is increasingly supported by Web 3.0 technology where all these stakeholders co create as online system users, with a username, something that has been labelled in the past user driven healthcare.43,44

This co creation has enabled healthcare educator practitioners to not only remain

focused on real patient care centred learning outcomes but develop realist ontologies

for medical education and practice such as "real patient OSCEs" that retain whole

patient clinical complexity rather than reduce it to smaller parts in order to optimize clinical complexity. 45

With recent changes in the school curriculum, there may be no need to again educate Medical Professionals about AI basics as they will come well prepared from schools, but to answer the real question about who/what will train the trainers at such a scale so fast, the logical answer from all that we have shared above appears to be: who else but Web 3.0 driven AI itself combined with Web 2.0 human looped critical realist heutagogy?

There are two key elements to the Critical Realist Heutagogic Approach:

Self-Directed Learning (Heutagogy): Encourages learners to actively question, analyze, and synthesize information rather than passively accepting claims. This aligns with higher-order Bloom’s Taxonomy skills (analysis, evaluation, creation) over rote

memorization.

Critical Realism: Focuses on uncovering underlying mechanisms and structural

biases.46

Present failure:

"Many if not most students come to the university in order to gain access to an eventual enfranchisement in 'the establishment,' not to critique and reject its privileges." Students want to gain, rather than to critique, positions of privilege, as encouraged by critical pedagogues."47

Hence out of 1000 students in the five batches of 200 each in our Narketpally medical college campus we can only hope to have 1-2 from each batch and mostly they don't reach us till their final years so we have just 1-2 out of 1000 to help us along with on an average 1 resident in a batch of 10 who may be able to develop self directed learning skills as a first step to critical realist heutagogy. As a result, the daily Web 2.0-driven team-based learnings in our groups comprise patients, student alumni from those 1-2 per batch, and occasional current residents who are sometimes forced into those groups from a service angle toward data capture and informational continuity.

In our bid to train some of our students to use critical realist heutagogy in a patient

centred manner we have tried to slowly make them graduate from an individual patient level to the system level to study the trajectory of a *disease* or a *phenotype* so that it promotes *proactive healthcare* instead of *reactive healthcare.* This aims to reduce patient harm and conserve expensive resources. (anecdotal rhetoric about what is best described as a minimalist but currently functioning prototype).

We look at PaJR patient data as deep phenotypes and chart their subsequent

trajectories through informational data continuity toward pattern recognition for

prediction. "Deep phenotyping represents a comprehensive approach to understand disease phenotypes by integrating detailed data from electronic health records (EHRs), clinical notes, and high-throughput technologies. Recent advancements in artificial intelligence (AI) and machine learning have significantly enhanced our ability to analyze and interpret complex phenotypic data, making it possible to uncover previously hidden

patterns and correlations. Techniques such as natural language processing (NLP), deep

learning, and network analysis are increasingly being used to extract and structure phenotypic information from unstructured data sources. These developments are particularly exciting as they pave the way for more precise disease classification and personalized treatment strategies."48

When we encounter a single patient and assess her past trajectory, we also have

similar past patient data where we have the entire completed trajectory and we then try

to match the current patient incomplete trajectory to see which patient scenario it fits

with closest at the stage where she has sought help and that can proactively reduce

harms suffered by the previously documented trajectories where we can take better informed decisions to avoid certain pathways.

These can be done through DL ML AI-driven 'case-based reasoning' CBR engines, and

to quote: "CBR solves problems by using or adjusting solutions to old problems. It is a

problem-solving paradigm that is fundamentally different from other major AI

approaches. Instead of relying solely on the general knowledge of a problem domain or

making associations based on generalized relationships between problem descriptors

and conclusions, CBR can use the specific knowledge of specific problem situations

(cases) experienced in the past to solve similar, new problems. A second important

difference is that CBR is an incremental, continuous learning method that retains new experiences in solving each problem and then applies it to solve new problems in the future."49

Currently using a syndromic approach to individual patient-centered medical education and research, we match these patient trajectories partly manually and partly automatically through qualitative thematic analysis using current-generation AI LLM-driven thematic analysis tools.

For example, if our currently encountered patient was one of the patients out of these

50 patients of anemia that we have archived from our EMR summaries, then one could

extract the themes from each patient 50 and check which of those matched our current

patient, similarly for altered sensorium here 51 and cardio renal failure here 52 and even for something as commonplace for trunkal obesity here.53

However, it's still a work in progress with a lot of non peer reviewed data building up

through primary https://pajr.in/ EMR data, which for the first time can be well analysed

by LLMs once we figure out a way to further automate this process seamlessly between

our primary patient Ux and the data processing Ux.

Future integrative resolution of current differences in the world views of Sturmberg's pragmatic, effective, person-centred, contextual, connected, syndromic, doing the right thing approach vs Ioannidis's population-based, single cause and effect, no-nonsense, doing things right approach vs Greenhalgh's non-mathematical value-driven approach:

Quoting from Section 5.3 of Sturmberg's paper, "The embrace of the pragmatic trial,"

aiming to show the variable (real world) effects of medical interventions should be

understood as a methodological approach to pattern identification.

Each of the outcome patterns can then be further explored to identify the potential

biological and environmental variables that contributed to ‘creating’ this pattern. The

next step will involve an exploration of the dynamic patterns (pathways) between the

variables of each pattern. Having identified the variables involved allows a specific focus

on studying potential ‘causal’ pathways, including any variability between patients in the same cluster."1

Sturmberg et al stress two key constructs to make this possible - (i) dynamic modeling

to find causal pathways between variables interacting over time, and (ii) complexity

theory - discovering emergent behaviour, feedback loops, et al. Figure 7 in their paper is a key diagram that explains this.

Most data analysis stops at the stage of clustering or finding relative significance of

variables, and what the authors appear to propose is to further do dynamic modeling on these variables over time to find causal pathways. The authors hope that complexity

theory when applied to this, will result in new treatment protocols in medicine.

Machine learning already has tools to do this, however, we haven't seen any paper or

project that uses all these tools together in the way the author explains in Figure 7.

Few examples of tools include (i) modeling dynamic Bayesian networks with tools such

as pgmpy https://github.com/pgmpy/pgmpy, and (ii) simulating multi-agent systems to

observe emergent behavior and feedback loops based on complexity theory, with tools

such as Mesa (https://github.com/projectmesa/mesa) or AgentTorch (https://github.com/AgentTorch/AgentTorch)

Applying this to our work, the CBBLE/PaJR process is a functional low-resource

settings prototype of a pragmatic trial - understanding patients with a 360 view,

conducting treatment with that 360 context, collaborative treatment and learning

process, and consented documentation as PaJR case report EHRs available open

access and logged daily in online learning portfolios archived here (ref PaJR dashboard). Very few health systems follow this construct. We see a huge opportunity

to follow the data analysis process mentioned in Sturmberg's paper and see if it indeed points to patterns that haven't been seen before or helps evolve new protocols not tried before. What is needed is well-formed input to current AI processing systems. For example, for creating dynamic Bayesian networks, or temporal graphs, we need the complete context of a patient broken down by parameters, the cause/intervention, and then the state of the patient again in the same or more parameters. This parameterization is the biggest challenge and will need the help of LLMs and doctors. Dr Van Der Shaar's work in Cambridge around Tempor AI

(https://github.com/vanderschaarlab/temporai) for healthcare data is also in the same

direction, although not at the level of what the paper is proposing.

Future of an albeit Orwellian, transparent, accountable, syndromic approach to medical

education and research and its ethical ramifications:

Medical professionals must prioritize afore mentioned case-based reasoning CBR,

healthcare information systems that regularly provide a transparent individual patient clinical audit in real time with rigorous evidence appraisal to avoid therapeutic missteps.54

Transparency in Research would not just remain confined to highlighting the importance of publishing all trial data, including negative results, but would insist on all making available contextual data surrounding each and every trial patient participant to inform clinical decisions.

Lessons for Medical Education:

Curriculum Shift: An individual patient-centred syndromic approach will enable moving beyond foundational knowledge (Bloom’s levels 1–2) to cultivate critical appraisal skills and teach students to dissect study designs, recognize bias, and assess real-world applicability.

Case-Based Learning: Using regular, connected, and contextual "real patient" data over simulations can better demonstrate the consequences of uncritical acceptance of

biased data.

Advocacy Training: Can empower future clinicians and their patients to challenge

misinformation and advocate for ethical research practices.

Key phrases and sentences as diagnostic markers (instead of major and minor criteria) for a unique medical education research syndrome: 

Syndrome as in it's etymology (Greek Sun Drammein, Sanskrit Sama Druma, together we flow), 

Collective medical cognition that thrives in connected contexts between individual patients and physicians flowing in and out of their lives like a river, 

Tools of collective medical cognition in Patient journey record PaJR, 

Case based blended learning ecosystem CBBLE (cable), 

User driven healthcare, UDHC, 

Critical realist heutagogy CRH, 

User driven learning community ontologies UDLCOs and 

AI driven case based reasoning CBR engines, 

Tree canopy (population based medicine, phase 2-3 clinical trials) stem connected to grounded tree roots (individual patient centered medicine, phase 1-4 trials), 

Translating positive learning outcomes to positive illness outcomes, 

Being together in the flow regularly with our patients and their journeys, 

Non local Quantum entangled theory of everything aka Everything is connected, 

Human ontologies as life to death trajectories connected through UDLCOs, 

Cyclical evolution and dissolution 

of humans and other beings

Whole-o-graphic systems where every fractal or factal broken through reductionism still shows the whole 

Those who know the whole actually don't and those who don't know it actually do (Kena upanishad and it's subsequent variations from Confucius to critical realism)57 

 

Research is to become a witness to truth, a stream/river of consciousness flooding our collective cognition and one can only perceive it if one can gather compassion and humility from everyone's life journeys!

Conclusion:

Borrowing from the learnings from Sturmberg's (1), Greenhalgh's (2), Ioannidis's (3) and our previous work, this current paper proposes a new design that will be able to: tie up every data element in an ontology framework driven by case-based reasoning engines and make them flow together in a future AI-supported whole-o-graphic ecosystem where every part is embedded in the whole such that when one breaks off different fragments from the whole, they find the whole still contained in the individual broken fragments!

The above rhetoric, including the one in the pre conclusion, is tied to the populist imagination of holography in current human

collective cognition and isn't scientifically impossible or untrue 55 and while case-based reasoning isn't part of the current populist rhetoric, it's rapidly growing pace, especially with deep machine learning techniques 56

.

Our paper urges a shift in the design of medical learning and research, from merely

trusting authority to embracing critical inquiry and yet maintaining a togetherness in our collective cognitive flow, concordant with ancient etymological definitions of 'syndrome.'

By integrating heutagogic principles and critical realism into individual patient-centred contextual inquiries, educators and researchers can better navigate complex, biased information landscapes, ultimately improving patient care through evidence-based, ethically informed decisions.

References:

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Practice. 2025 Feb;31(1):e14139.

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ACKNOWLEDGEMENTS:

To all our patients who we encountered in Narketpally, virtually from afar and also those who physically visited us in Narketpally from 2 kms away as well as 2000 kms away from different parts of India.

To all our students formal and informal who elected to work with our individual patient projects not just as a formal curricular task but an elective driven by passion

While the substantive research, analysis, and conclusions presented here are entirely the work of the authors, we

acknowledge using an AI language model to help polish language, streamline structure, and explore alternative expressions. All AI-generated text has been carefully reviewed, edited, and approved by the authors to ensure

accuracy, clarity, and consistency with our scholarly intent. The visual figures have also been modified using AI softwares however, the original source and content was the sole work by authors of the chapter.

Peer review Feedback: 

[07/05, 09:31] rb: Thanks for sharing this and stimulating me to revisit our old CBBLE paper

https://pmc.ncbi.nlm.nih.gov/articles/PMC6163835/

and review it again in the context of our current CBBLE paper @rk 

So even now when we talk of a syndromic precision approach by clustering individual contextual data we are perhaps close to real life although nowhere near accuracy but then what is accuracy? 

Accuracy mandates goal directed cognition, such as if "together we flow" then which direction should we flow and why and where are questions that determine our quest for "accuracy" but in the long term every individual human trajectory that flows can only flow from point "b" (aka birth) to point "d" (aka death)? In between they may have short term goals and short term struggles to achieve accuracy in whatever catches their fancy at that point of time?

👆apologies for the stream of consciousness prose

[07/05, 09:35]cm: Themes:

1. *Complexity of accuracy*: Accuracy can be context-dependent and influenced by individual goals and values.

2. *Human trajectory*: The inevitability of birth and death frames our lives, while short-term goals and struggles shape our experiences.

3. *Direction and purpose*: Determining the direction and purpose of our "flow" is a fundamental question.

There is a dynamic interplay between individual agency, context, and the pursuit of meaning. 

👆🏻Meta AI

[07/05, 12:11]rk: Very deep indeed! These could be interesting research questions - 

1. Is accuracy absolute or contextual? 

2. Is contextual accuracy better for personalized healthcare? 

3. Is direction more important than goal? 

4. Are micro steps in the right direction better than a major step in a locally optimized direction but may not be aligned to the direction long term.

This has so many great ideas in it – you could turn it into a book. I asked Perplexity to comment on the draft, and then asked it to rewrite it – the output is inserted into the draft for your consideration. I put my own thought into the “comments” in track changes. This is a very quick comment only – to allow you to continue your work. Happy to provide you with further comments on a later version.

Cheers

Narketpally syndrome and the embedding of contextual values in real life patient pathways 

Integrating Contextual Values in Healthcare: A Syndromic Approach to Patient-Centered Learning in Narketpally

Introduction

The fragmentation of medical education, research, and practice-disconnected from the contextual needs of patients and providers-remains a critical challenge. Building on Sturmberg & Mercuri’s (2025) framework 1, Greenhalgh’s systems critique, and Ioannidis’ methodological insights, this paper proposes a patient-centered model grounded in seven years of experiential practice at Narketpally, India. We demonstrate how case-based reasoning and systems thinking can bridge reductionist gaps through a syndromic ("together we flow") approach.

Comment inside the feedback: I probably would start with a 1-2 sentence case illustration to ask the question – how do we best study this problem (and then link everything else to : HOW and WHY do our methods work, and WHAT does it teach us about knowledge generation for patient-centered health care  

Outpatient care: 55-year-old woman with anxiety tied to familial trauma → Emotional context as therapeutic lever

[Case 10/4/2025]

Inpatient care: Comatose patient with Narketpally’s "invisible pillow sign" (Figure 1) → Recovery despite diagnostic uncertainty

Theoretical Framework

Reductionism vs. Holism

Contemporary research often isolates variables, stripping individual patient context 1. 

Our model recenters the "greater whole" by integrating:

Etymological foundations: The term syndrome (Greek sundromē: sun- "together," dramein "to flow") and its Sanskrit root druma ("tree/river") inform our dual metaphors:

River imagery: Knowledge integration across education, research, and practice

Tree imagery: Individual patient journeys (roots) nested within population health (canopy)

Methodology: Critical Realist Heutagogy

Our Case-Based Blended Learning Ecosystem (CBBLE) combines:

Patient Journey Records (PaJR): Collaborative documentation of illness trajectories

Web 2.0/3.0 tools: Real-time case analysis with AI-supported pattern recognition

Ethical rigor: HIPAA-compliant data anonymization

Results: Thematic Outcomes

Population-Based Insights

Theme

Example

Impact

EBM Value Translation

Wildebeest model analogizing absolute vs. relative risk

Highlighted communication gaps

Educational Biases

Rote memorization’s link to pharma influence

Proposed curriculum reforms

Patient-Centered Outcomes

Outpatient care: 55-year-old woman with anxiety tied to familial trauma → Emotional context as therapeutic lever [Case 10/4/2025]

Inpatient care: Comatose patient with Narketpally’s "invisible pillow sign"

(Figure 1) → Recovery despite diagnostic uncertainty

Discussion: Narketpally Syndrome Reimagined

From Literal to Conceptual

Originally describing skeletal fluorosis, "Narketpally syndrome" now embodies:

Global→Local learning: Web 3.0-enabled knowledge transfer

Uncertainty management: Co-created ontologies via critical realist heutagogy

Transparent ecosystems: PaJR’s lifelong learning architecture

Future Directions

AI integration: TemporAI for dynamic Bayesian networks1

Curriculum reform: Real-patient OSCEs preserving clinical complexity

Conclusion

By harmonizing Sturmberg & Mercuri’s pragmatism, Ioannidis’ rigor, and Greenhalgh’s values, we propose a holographic model where patient narratives and population data coexist symbiotically. This syndromic approach-rooted in Narketpally’s experience-offers a template for reconciling reductionism with holism through AI-augmented, patient-driven ecosystems.

Key Improvements:

Structure: Streamlined sections with tables/bullet points for clarity

Metaphors: Consistent use of river/tree imagery without overextension

Citations: Standardized Vancouver referencing (no URLs)

Theory-Practice Links: Explicit connections between frameworks and case examples

Visuals: Integrated figure references (e.g., Figure 1: ligamental sign)1 Sturmberg et al., 2025; Greenhalgh, 2024; Ioannidis, 2025... (Full reference list appended)