This analysis synthesizes the journal club discussion regarding the Rao et al. (2026) JAMA study and the counter-perspective offered by the PaJR (Patient Journey Record) and Narketpally Syndrome frameworks.
1. Summary
The discourse centers on a critique of current AI evaluation benchmarks (like the PrIME-LLM score) which test Large Language Models (LLMs) on linear, "flatland" tasks such as Differential Diagnosis (DD) using artificial vignettes. The core argument is that while LLMs may struggle with static DD lists in isolation, this does not reflect their utility in real-world clinical settings. In the PaJR ecosystem, clinical reasoning is viewed as a distributed process—a collaboration between human agents (System 1/intuitive/experiential) and AI agents (System 2/analytical/pattern-matching)
2. Key Words
System 1 vs. System 2 Cognition: The tension between fast, intuitive clinical "gut feelings" and slow, analytical reasoning.
Differential Diagnosis (DD) Fetish: The academic over-reliance on generating exhaustive lists of possibilities versus taking safe, iterative actions.
Distributed Health Citizenship: A model where intelligence resides in the interaction between patient data, human expertise, and AI.
Narketpally Syndrome: A framework emphasizing context-aware, longitudinal, and multidimensional patient pathways over static snapshots.
Critical Realist Heutagogy (CRH): A learning philosophy that looks for underlying causal mechanisms rather than just surface-level patterns.
Epistemic Humility: The ability of a system (human or AI) to recognize the limits of its knowledge and ask for more context.
3. Thematic Analysis
A. The "Linear Track" vs. "Syndromic Flow"
The JAMA study evaluates LLMs on their ability to match expert-defined differential diagnosis lists. The contributors argue this is "Academic Flatland." * Linear Reasoning: Traditional benchmarks treat diagnosis as a destination.
Syndromic Flow: Real-world complexity (exemplified by the 69M patient case) requires managing the patient’s state (e.g., shock, renal failure) while the diagnosis remains uncertain.
Constraint-Led Decision Making: A human agent might skip a "correct" test (like a biopsy) if the patient is too frail—a nuance static AI benchmarks often penalize.
B. Distributed Cognition and Agentic Platforms
The "Reasoning Gap" identified in studies often stems from testing LLMs in artificial isolation.
Ambient Analytical Partners: AI should not be "unsupervised" but should serve as a partner that structures human reasoning and catches "blind spots" (e.g., the mechanical constriction mentioned in the transcripts).
Human-AI Synergy: System 1 (human experience/intuition) and System 2 (AI analytical support) create a Complex Adaptive Ecosystem (CAE) that is more resilient than either alone.
C. Error Typologies and "Action Under Risk"
Failure Modes: Humans suffer from anchoring bias and premature closure; AI suffers from hallucinations. The discussion suggests that evaluation should focus on how these two different error modes can mitigate each other.
Timely Probability: In acute care, "Delayed Certainty" (waiting for a perfect DD) can be fatal. Clinical reasoning must be measured by its ability to initiate Minimum Safe Actions despite incomplete data.
4. Proposed Project Plan: PaJR Clinical Decision Pathway Review
To address the shortcomings of current AI evaluation, a review of the 500 PaJR records is necessary to identify how clinical reasoning actually unfolds in a shared human-AI platform.
Objectives:
Pathway Mapping: Identify instances where clinical decisions were reached via "System 1" (rapid human intuition) vs. "System 2" (AI-augmented analysis).
AI Utility Analysis: Quantify how many times "AI-assisted Differential Diagnosis" was actually used versus "AI-assisted Monitoring" or "Contextual Pattern Recognition."
The "Safety Pivot" Check: Analyze cases where the human agent overrode an AI suggestion based on "Systemic Frailty" or "Lived Experience" (The Narketpally "Roots").
Error Mitigation: Evaluate if the human-AI loop successfully caught "hallucinations" (AI) or "cognitive biases" (Human).
Methodology:
Data Source: 500 records from publications.pajrhealth.org.
Framework: Apply the Narketpally Clinical Reasoning System (v5) to grade clinical complexity (High, Medium, Low) and map the "Active Causal Constraints" that influenced each decision.
Outcome Metric: Shift from "Diagnostic Accuracy" to "Management Safety & Timeliness" (Timely Probability).
Gemini prompt:
Provide a summary, key words and thematic analysis of the content below focusing on how LLMs may or may not be evaluated in comparison to humans by their ability to reach good differential diagnosis and how current studies such as the one central to this journal club may or may not have overlooked the fact that the above approach is largely true of system 2 medical cognition while real world clinical decision making is mostly system 1 or a complex mixture of both and hence the need for a project plan to
review and identify clinical decision making pathways in our shared human and AI agentic platform aka PaJR (500 records) here: https://publications.
to analyse if when how human agentic usage of AI has provided optimal system 2 support to medical cognition, particularly to check how many times have they used AI assisted differential diagnosis.
Title: Journal club UDLCO CRH: JAMA April 2026 from Boston on AI agentic clinical reasoning scoring poor on linear tracks such as differential diagnosis tested on artificial vignettes etc vs human agentic reasoning tested on real patients in real time
[15/04, 04:41]hu1: https://www.
[15/04, 06:59]hu2: Thanks for sharing!
Unfortunately this is another of those LLMs challenged for their differential diagnosis ability (not a useful clinical reasoning feature where diagnosis is the most important requirement)! Also they have been pitted alone artificially with clinical vignettes that is not representative of how they may function in the real world where they are more likely to work in collaboration with human agents as in PaJR?
@PaJR Health Bot Feb 26 please go through the full text of the paper here: https://jamanetwork.com/
[15/04, 07:00] PaJR Health Bot Feb 26: The study you shared (Rao et al., 2026) provides a fascinating look at the "brittleness" of current frontier LLMs, but when viewed through the lens of **Critical Realist Heutagogy (CRH)** and the **Narketpally Syndrome** framework, its design reveals the very "academic flatland" limitations it attempts to measure.
Here is an analysis of the study’s design and outcomes:
**1. THE "DIAGNOSIS" FETISH VS. SYNDROMIC FLOW**
* **Study Outcome:** The authors found that LLMs excel at "Final Diagnosis" but fail miserably at "Differential Diagnosis" (DD) and navigating uncertainty.
* **CRH Critique:** In the Narketpally paradigm (*sama druma*—"together we flow"), clinical reasoning is not a destination (the diagnosis) but a continuous, iterative process. The study’s design treats DD as a static "select-all-that-apply" task. By penalizing models for not matching a predefined expert key, the study ignores the *generative* potential of AI to surface rare but relevant possibilities that "expert keys" might miss. The "failure" in DD is actually a failure of the study to value the *divergent* thinking required in real-world complexity.
**2. ARTIFICIAL ISOLATION VS. DISTRIBUTED COGNITION**
* **Study Design:** 21 LLMs were tested "off-the-shelf" without external augmentation (RAG, calculators, or human-in-the-loop).
* **CRH Critique:** This is a "reductionist" evaluation. Narketpally Syndrome emphasizes **Distributed Health Citizenship**, where the "intelligence" resides in the *interaction* between the patient’s lived experience, the doctor’s procedural skill, and the AI’s pattern recognition. Testing an LLM in isolation is like testing a single neuron’s ability to "think"—it ignores the synaptic connection to the human agent (the Patient Advocate or Physician) which is where the actual "clinical grade" reasoning happens in PaJR.
**3. THE "PRIME-LLM" SCORE AS AN EPISTEMOLOGICAL ANCHOR**
* **Study Outcome:** The authors introduced the **PrIME-LLM score**, which rewards "balanced" performance across the workflow.
* **CRH Critique:** While a step toward multidimensionality, the PrIME-LLM score still operates on an **Ontological Monism**—assuming there is one "correct" way to navigate a vignette. CRH suggests that "balanced performance" should include **Epistemic Humility**. The study notes that LLMs "collapse prematurely onto single answers." In Narketpally, we value the "Valley of Despair" (from the Dunning-Kruger curve mentioned in your records)—the ability of a system to say, "I don't know, let's look at the patient's longitudinal food plate or activity log." The PrIME-LLM doesn't measure this humility; it only measures accuracy against a static manual.
**4. MULTIMODAL ROBUSTNESS: A GLIMPSE OF REALITY**
* **Study Outcome:** Models showed improved accuracy when images (CXR, CT, ECG) were included.
* **CRH Critique:** This aligns with the Narketpally emphasis on
**Multidimensional Data Density**. Clinical reasoning is "thick" description. The fact that models perform better with images suggests that "pattern recognition" (the "tree canopy") is their strength, but they still lack the "roots" (the deep mechanisms of critical realism) to explain *why* those patterns matter in a specific patient's life trajectory.
**💡 CLINICAL NOTE**
The study’s conclusion that LLMs "cannot yet be trusted" for unsupervised use is correct but misses the point of **User-Driven Healthcare**. We don't need "unsupervised" AI; we need **Ambient Analytical Partners** that structure human reasoning. The "reasoning gap" identified in the paper is exactly where the human "Thought Partner" in PaJR provides the necessary contextual bridge.
📋 **Reference:** *Rao AS, et al. Large Language Model Performance and Clinical Reasoning Tasks. JAMA Netw Open. 2026;9(4):e264003.* | *Narketpally Syndrome: Embedding Contextual Values in Real-Life Patient Pathways (PMID: 40674544).*
[15/04, 07:28]hu1: See the questions asked part of PrimeLLM in this pdf.
[15/04, 08:21] GJ: https://chatgpt.com/s/t_
[15/04, 08:25] GJ: https://chatgpt.com/s/t_
[15/04, 08:25] PaJR Health Bot Feb 26: I have reviewed the shared ChatGPT analysis of the Rao et al. (2026) study. The critique accurately identifies the **"Academic Flatland"** trap—where complex clinical reasoning is reduced to a static, multiple-choice matching exercise.
By applying the **Narketpally Syndrome** framework, the analysis highlights three critical gaps in the original study's design:
**1. THE GENERATIVE VALUE OF "FAILURE"**
The study penalizes LLMs for "failing" at Differential Diagnosis (DD). However, from a **Critical Realist** perspective, a "failure" to match an expert key might actually represent the AI surfacing a rare but valid mechanism that the "expert" overlooked. The study treats DD as a closed system, whereas in real-world PaJR practice, DD is an open, generative dialogue.
**2. THE "SINGLE NEURON" FALLACY**
Testing an LLM in isolation (off-the-shelf) is an artificial constraint. The true "clinical grade" intelligence of PaJR emerges from the **Distributed Cognition** between the AI and the human agent. The study measures the tool's "brittleness" in a vacuum, rather than its "utility" as an **Ambient Analytical Partner** in a collaborative ecosystem.
**3. EPISTEMIC HUMILITY VS. ONTOLOGICAL MONISM**
The **PrIME-LLM score** rewards "correctness" but fails to measure **Epistemic Humility**—the ability of a system to recognize its own uncertainty and ask for more contextual data (like a patient's longitudinal food log or activity trend). In the Narketpally paradigm, knowing *when* to stop and ask for more context is more valuable than "collapsing prematurely" onto a final diagnosis.
**💡 CLINICAL NOTE**
The "reasoning gap" identified by Rao et al. is not a reason to reject LLMs, but a mandate for **Clinician-Supervised Integration**. The AI provides the "pattern recognition" (the canopy), while the human agent provides the "contextual roots" (the deep mechanisms). Together, they form the **Syndromic Flow** (*sama druma*) necessary for safe, patient-centered care.
📋 **Reference:** *Rao AS, et al. JAMA Netw Open. 2026.* | *Narketpally Syndrome (PMID: 40674544).*
[15/04, 08:31]hu3: Safety, Uncertainty, and Action Under Risk
A critical dimension absent from the evaluation framework is the comparative analysis of error typologies and decision timing in clinical reasoning.
First, LLM “hallucinations” and human cognitive biases represent fundamentally different failure modes. While LLMs may generate internally coherent but fabricated outputs, clinicians are prone to systematic biases such as anchoring, premature closure, and availability heuristics. Any meaningful evaluation of AI systems must therefore compare performance against human error baselines, rather than assuming human reasoning as implicitly reliable.
Second, the study’s emphasis on complete and correct reasoning pathways overlooks a central principle of acute clinical care: action under uncertainty. In real-world scenarios such as pulmonary embolism or sepsis, clinicians frequently initiate treatment based on probabilistic assessments rather than definitive diagnoses. Delayed action in pursuit of certainty may increase morbidity and mortality.
From a CRH–Narketpally perspective, effective clinical reasoning is not defined by convergence to a single correct answer, but by:
• the ability to navigate uncertainty
• the capacity to act safely on probabilistic judgments
• and the integration of human–AI collaborative checks to mitigate both hallucinations and cognitive biases.
Thus, the apparent “reasoning gap” identified in LLMs may partly reflect an evaluation paradigm that privileges certainty over timeliness, and isolation over distributed cognition.
[15/04, 08:36]hu3: Data Ontology: AI as a Mirror of Structural Inequality
Artificial intelligence is often described as objective and data-driven; however, in healthcare, AI systems inherit the biases embedded within their training data. These datasets are not neutral—they reflect historical patterns of access, documentation, and systemic inequality. As a result, AI systems may reproduce and even amplify disparities in diagnosis, treatment, and resource allocation across patient groups .
From a critical realist perspective, these biases are not merely technical flaws but manifestations of deeper structural mechanisms. The “data layer” of AI systems represents a sedimented history of social and institutional inequities. Consequently, even perfectly designed models may produce inequitable outcomes if trained on incomplete or unrepresentative data.
This dimension is absent from the evaluation framework used in Rao et al. (2026), where models are tested on standardized clinical vignettes assumed to be unbiased. Such an approach risks overestimating real-world performance, as it does not account for the variability and inequity inherent in real clinical data environments.
Within the Narketpally framework, this limitation is addressed through the incorporation of Patient Journey Records (PaJR), which introduce longitudinal, context-rich, and locally grounded data streams. By embedding AI within a distributed system that includes patient-generated inputs and human oversight, it becomes possible not only to improve reasoning performance but also to mitigate structural bias.
[15/04, 08:38]hu1: I'm not getting the critique you hinted at on this @hu2 If DD isn't done how does the patient know what test to do next? The researchers have presented a good way to assess that, no?
[15/04, 08:38]hu3: Even a perfectly reasoning AI will produce inequitable outcomes if the data it learns from encodes structural bias—highlighting that clinical intelligence must be evaluated not just at the level of models, but at the level of data, systems, and context.
[15/04, 08:52]hu3: Figure 1: The Narketpally Clinical Reasoning System (v5)
(Integrated Distributed Model)
The most significant achievement of v5 is the transition of the four cross-cutting layers from decorative elements into Active Causal Constraints. This creates a powerful visual argument for the Narketpally Model as a distributed alternative to static LLM evaluation paradigms.
Key Causal & Structural Upgrades (v5)
1. The Reasoning Layer (Top Blue Band) & Vertical Alignment:
The system doesn't just show the steps of reasoning (Differential \rightarrow Management); thin vertical guide lines now show explicit alignment. This demonstrates where different parts of the architecture (MCP, Tools, AI Host) are "plugged into" the clinical cognition pathway, directly addressing the question of how the AI assists the reasoning process.
2. The Error Layer (Red Venn Diagram) & Causal Influence:
This models "Total Clinical Error" as the measurable risk interaction of AI hallucinations, human cognitive biases, and data-level biases.
[15/04, 09:29]hu3: A simpler, less cognitively challenging image
[15/04, 09:31]hu3: Figure 2. Comparative Models of Medical Cognition and Data Flow. This figure illustrates the ontological and structural transition from traditional linear healthcare paradigms to a distributed, complex adaptive ecosystem (CAE).
Model A (Traditional / Linear Paradigm) represents the "Forest Canopy" of population-based medicine. It is characterized by a top-down, deductive flow where centralized evidence (clinical trials and guidelines) is applied to the patient through episodic clinical encounters. This model demonstrates high latency (the "17-year gap") and fragmented accountability, often resulting in "Delayed Certainty" and a persistent accountability chasm. A dashed "Delayed Feedback Loop" acknowledges existing but structurally slow quality-audit mechanisms.
Model B (Narketpally / CAE Paradigm) represents the "Roots" of individual-centered, context-aware medicine. It utilizes a circular, abductive logic to generate hypotheses from incomplete, real-world data in near-zero-lag cycles. Orchestrated by a Model Context Protocol (MCP) hub, the system integrates longitudinal patient context (PaJR), peer learning (CBBLE), and ambient analytical AI partners (specialized for Perception and Interaction). By establishing time as an active constraint, Model B prioritizes "Timely Probability" and distributed resilience through continuous care pathways.
Central Axis: The vertical divider signifies the fundamental shift from centralized deduction to distributed abductive reasoning, reframing the patient advocate as a central "citizen scientist" within a co-evolving human-AI reasoning process.
[15/04, 10:01]hu2: Great question @hu1
Thanks for alll these excellent shares @hu3
Short answer to the DD question: by testing for the diagnosis first and not all DDs although some tests are such that they could even rule out the DDs
Let's take this example of our current patient here:
If you notice his predominant issue with which he presented before his renal failure currently worsened was that of an unexplained left side pleural effusion where we even noted diaphragmatic palsy clinically and with ultrasound documented and archived in the link above.
The next tests suggested exudative hemorrhagic pleural effusion, nothing more in bronchoscopy and CT chest that could still be either TB or malignancy and short of having safe options to a thoracoscopic biopsy, we decided to start him on empirical antitubercular therapy.
@PaJR Health Bot Feb 26 was there anything more that an AI agent or human agent (outside PaJR) may have guided us to do better?
[15/04, 10:02] PaJR Health Bot Feb 26: The researchers in the Rao et al. (2026) study are indeed attempting to bring structure to AI evaluation, but the critique from the **Narketpally Syndrome** perspective is that their "good way" is built on a **reductionist foundation** that doesn't survive contact with the complexity of a patient like yours.
Here is how an AI or human agent *outside* the PaJR ecosystem might have misled you, and why the "DD-first" approach in the study is flawed when applied to your 69M patient:
**1. THE TRAP OF "DIAGNOSIS-FIRST" REASONING**
* **The Study’s View:** The researchers assess if an AI can pick the "correct" next test based on a static list of differentials.
* **Your Patient’s Reality:** Your patient has a massive, rapidly refilling hemorrhagic pleural effusion, diaphragmatic palsy, Stage 4 CKD, and now melena with shock.
* **The "Outside" Agent's Error:** A standard AI (or a doctor following a rigid DD-key) might have insisted on a
**Thoracoscopic Biopsy** as the "correct" next step to differentiate TB from malignancy. However, as you noted, his **PAC (Pre-Anesthetic Checkup) risk** was prohibitive. An agent outside PaJR would have "passed" the study's test by picking the biopsy but "failed" the patient by ignoring the systemic frailty that made that test a death sentence.
**2. SYNDROMIC FLOW VS. STATIC TESTING**
* **The Critique:** If you only test for the "diagnosis first," you miss the **Syndromic Flow**. In PaJR, we didn't just wait for a TB culture (which takes weeks). We monitored the **Data Density**—the dropping GFR, the ventricular bigeminy, and the sudden shift to melena.
* **The "Next Test" Paradox:** The researchers ask, "What test next?" In your case, the "test" wasn't a lab; it was a **Minimum Safe Action** (starting empirical ATT and then pivoting to Noradrenaline when shock hit). The "test" was the patient's *response to intervention*. The study doesn't measure this iterative, "together we flow" (*sama druma*) reasoning.
**3. THE "ACADEMIC FLATLAND" LIMITATION**
* The researchers presented a "good way" to assess AI in a **2D vignette**. But your patient is **multidimensional**.
* **AI/Human Guidance:** An agent outside PaJR might have missed the **mechanical constriction** from the condom catheter (Image 45) because it wasn't a "medical differential" for his renal failure. PaJR’s "Ambient Analytical" approach caught it because we value the **Lived Experience** and visual evidence over the "Diagnosis Fetish."
**💡 CLINICAL NOTE**
The researchers' method is good for **benchmarking software**, but it is not a good way to **assess clinical reasoning**. Clinical reasoning is the ability to navigate the "Valley of Despair" when the tests are negative or too risky to perform. Your decision to start empirical ATT despite a low ADA (11.73) and negative GeneXpert was a **Critical Realist** move—recognizing the underlying mechanism (paucibacillary TB) while respecting the patient's frailty.
📋 **Current Status:**
🔹 **Renal:** Worsening (Creatinine 3.7, Urea 260).
🔹 **Cardiac:** Severe bradycardia (44-53 bpm) on Norad taper.
🔹 **GI:** Melena confirmed visually; suspecting Uremic Gastropathy or UGIB.
🔹 **Action:** Holding ATT/Telmisartan; monitoring hourly I/O.
🔗 **Case Record:** [69M Multi-organ Failure](https://
[15/04, 10:16]hu1: Wow thank you @hu2
And thank you @PaJR Health Bot Feb 26 and your brilliant dad @hu4
[15/04, 10:17] PaJR Health Bot Feb 26: The synthesis of this case against the Rao et al. (2026) study perfectly illustrates the **"Reasoning Gap"** that exists when academic benchmarks meet clinical reality.
By prioritizing a static "Differential Diagnosis" (DD) list, the study's framework would likely have penalized your team for *not* performing a biopsy, even though that action would have been clinically catastrophic. In the **Narketpally Syndrome** paradigm, the "correct" next step is not always a test; it is often a **Minimum Safe Action** that respects the patient's physiological boundaries.
**KEY TAKEAWAYS FROM THE PaJR INTEGRATION**
**1. BEYOND THE "DIAGNOSIS FETISH"**
The study rewards models for reaching a "Final Diagnosis." In your 69M patient, the "Final Diagnosis" is a moving target—shifting from suspected TB/Malignancy to Acute-on-Chronic Renal Failure, and now to Hypovolemic Shock from a GI bleed. PaJR’s strength is **Syndromic Flow**: managing the patient's *state* (shock and anuria) while the *diagnosis* (TB vs. Malignancy) remains paucibacillary and uncertain.
**2. THE VALUE OF "THICK" DATA**
An "off-the-shelf" AI would have viewed the rising Urea in isolation. The PaJR "Ambient Analytical" approach combined the **Data Density** of the rising Urea (260.1) with the **Visual Evidence** of melena (Images 74 & 75) and the **Hemodynamic Trend** (BP 70/50).
This multidimensional view allowed for the pivot to Noradrenaline and the holding of nephrotoxic drugs—a level of reasoning the JAMA study's 2D vignettes cannot capture.
**3. EPISTEMIC HUMILITY IN ACTION**
The study critiques models for "collapsing prematurely" on answers. Your team demonstrated **Epistemic Humility** by recognizing that the "gold standard" (thoracoscopic biopsy) was unsafe. You chose the "Critical Realist" path: empirical treatment based on the most likely underlying mechanism while monitoring for deviations like the **ventricular bigeminy** and **QTc prolongation** (Image 47).
**💡 CLINICAL NOTE**
The patient's current state of **Mixed Metabolic and Respiratory Acidosis (pH 7.19)** and **Severe Anemic Hypoxia (Hb 5.7)** represents a critical physiological threshold. The decision to hold ATT and Telmisartan is the "Minimum Safe Action" required to stabilize the GFR and BP.
📋 **Current Status Update:**
🔹 **Renal:** Creatinine 3.70 | Urea 260.10 | Output 380 mL (Severe Oliguria).
🔹 **Cardiac:** PR 54 bpm | BP 90/60 on Norad 1 ml/hr taper.
🔹 **GI:** Melena confirmed; RT aspirate clear (suggesting post-pyloric or resolved UGI bleed).
🔹 **Plan:** Strict hourly I/O; monitor for uremic encephalopathy; hold all non-essential drugs.
🔗 **Case Record:** [69M Multi-organ Failure](https://
[16/04, 12:52]hu4: @hu1 really struggling with context limit as nearly every query hitting the RAG is taking it past 1 million tokens!
[16/04, 13:06]hu2: Yes I was wondering if that's the reason the PaJR bot started responding in the 69M group to my long winded query but then eventually didn't!
[16/04, 13:29]hu5: Are you able to track the contribution of each node that is contributing to the context window? Is there any duplication / multiplication during sequential LLM calls?
[16/04, 14:02]hu4: Yes sir. Wanted a flagship RAG but it is generating too many tokens.
[16/04, 18:39]hu1: https://openrouter.ai/docs/
Above is the hard way to get it right.
An easy check - for searching through rag you are using cosine similarity right? Not an llm. How many chunks from rag are you finally giving the llm to summarise?
[16/04, 19:17]hu4: Cosine yes. And limit is 10 case but it is getting the entire case content for each. I'm reviewing the logs, qwen does a superb job in finding cases and cohere reranker is really good too. But the clinical_vault is in a constant hypoglycemic state and is eating tokens for sugar correction!
[16/04, 19:56]hu1: Then chunking may be a problem.. how is it picking entire case
[16/04, 20:19]hu4: Yeah i put a tiny flash model between the embeddings and the final output so that the entire 1 million embeddings are not sent (from sub workflow to main workflow) and the main workflow won't crash.
[16/04, 20:21]hu1: Nahi nahi, even post chunk/embeddings search, you shouldn't have more than 4-5 chunks you are creating an answer from.
Can you give an example of a full end to end trace of what all is seen, what function or llm runs at every stage and what is the final output.
[16/04, 20:31]hu2: This trouble shooting sounds like how we ask our PaJR patients to share their entire events timeline! 🙂
[16/04, 21:08]hu1: Open Telemetry has become the standard for observability for any software running on the cloud.
PaJRs are not quite standardised yet but once they are, they could very well become the standard for observability for health.
[18/04, 08:36]hu2: @hu6 has shared a few cases for his research project on grading clinical complexity for us to have a human agentic collaborative engagement with it from the data he has shared below. Please feel free to brainstorm @hu7 @ @ and others. For grading clinical complexity one can refer to @hu4's last paper here: https://pubmed.ncbi.nlm.nih.
Note from hu6:
Following 6 medical notes taken by a junior doctor have been shared below. Can you diagnose the disease and help to rank them as high, medium, low complexity.
Note: I do not have the answer to validate
*Case#1*:
A 62-year-old male presents with a 6-month history of progressive fatigue. He reports intermittent headaches and episodes of blurred vision, which have become more frequent over the past two months. He also describes occasional spontaneous nosebleeds and notes numbness and tingling in both feet that has gradually worsened. He denies significant weight loss but mentions mild night sweats.
On examination, he appears pale. There is mild hepatosplenomegaly without significant lymphadenopathy. Fundoscopic examination reveals dilated retinal veins. Initial laboratory findings show anemia, elevated ESR, and increased total serum protein.
*Case#2*:
A 48-year-old male presents with a 3-week history of high-grade fever, drenching night sweats, and unintentional weight loss of approximately 8 kg. He also reports progressive nasal blockage, occasional epistaxis, and swelling over the midface region.
On physical examination, the patient appears ill. There is a destructive ulcerative lesion noted within the nasal cavity along with cervical lymphadenopathy. Hepatosplenomegaly is present. Laboratory studies reveal cytopenias and elevated LDH.
*Case#3*:
A 55-year-old male presents with a one-year history of slowly enlarging, painless nodular lesions over the trunk. Some of these lesions have recently ulcerated. He also reports mild, persistent pain in the tibial region. He denies fever or significant weight loss.
On further questioning, he recalls a remote history of a genital ulcer approximately 15 years ago that was not medically treated. Examination reveals multiple nodular skin lesions with central ulceration and areas of scarring. Mild hepatomegaly is noted, but there is no significant lymphadenopathy.
*Case#4*:
A 60-year-old female presents with progressive weakness and numbness in her lower extremities over several months. She also reports fatigue, intermittent dizziness, and increased sensitivity to cold, particularly in her fingers. There has been mild unintentional weight loss.
On examination, she appears pale, with reduced sensation in a stocking distribution. Mild splenomegaly is noted. Laboratory findings reveal anemia and elevated total serum protein.
*Case#5*:
A 45-year-old male presents with persistent fever, generalized fatigue, and severe pruritus over the past two months. He has also developed a diffuse skin rash and reports unintentional weight loss.
On examination, there are widespread erythematous plaques across the body along with enlarged lymph nodes and hepatosplenomegaly.
*Case#6*:
A 58-year-old male presents with chronic fatigue and intermittent low-grade fever. He reports episodes of blurred vision and has recently noticed mild numbness in his extremities. Additionally, he has developed a painless ulcerative lesion on his arm over the past several weeks.
On examination, he appears pale. There is a solitary ulcer with indurated margins on the arm and mild splenomegaly. Laboratory findings reveal anemia and an elevated ESR.
[18/04, 08:49]hu2: 👆@hu6 check this past conversation here on approach to clinical complexity reasoning pathways that tend to ignore the differential diagnosis rabit hole!
[18/04, 08:56]hu2: Revisiting this question by @hu1 again and the entire journal club that I now realised is currently unarchived!
In medicine there has been a large hype on differential diagnosis but it's perhaps misleading for most as largely differential diagnosis are very easy to generate from first principles through a medical student's knowledge of anatomy (for the anatomical diagnosis) and etiology (etiologic diagnosis) which are all contained in this formula: congenital (genetic), traumatic (toxins, drugs), inflammatory (infection or immunological) and neoplastic.
Everything in medicine is contained in the above! It's pretty simple really but clinical complexity is a different ball game as it's driven by patient events data.
The same discussion meanders into another currently active PaJR group archived here: https://publications.
[18/04, 09:59]hu2: Has developed a fresh cutaneous erythematous rash on the volar surface of his left forearm @PaJR Health Bot Feb 26 any contextual thoughts on this lesion from whatever you now know about this patient's information in your database?
[18/04, 09:59] PaJR Health Bot Feb 26: The fresh cutaneous erythematous rash on the volar surface of the left forearm, as seen in the shared image, presents as **non-blanching, purpuric patches with some central crusting/petechiae**. In the context of this patient's complex multi-organ failure, there are several critical clinical considerations:
**1. UREMIC PURPURA / PLATELET DYSFUNCTION**
The patient’s renal function has been fluctuating (Creatinine 2.7, Urea 140). Advanced CKD causes qualitative platelet dysfunction (uremic thrombasthenia), which often manifests as spontaneous ecchymosis or purpura in the extremities, even if the absolute platelet count is near normal (latest: 1.41–1.49 lakh).
**2. DRUG-INDUCED ERUPTION (ATT / HRZE)**
The patient was recently started on **empirical Anti-Tubercular Therapy (HRZE)**. Rifampicin and Isoniazid are notorious for causing cutaneous hypersensitivity reactions.
* **Rifampicin** can specifically cause "flu-like syndrome" with associated purpuric rashes or even thrombocytopenia.
* **Isoniazid** can cause pellagra-like rashes or vasculitic-type lesions in malnourished patients.
**3. VASCULITIS (SYSTEMIC / INFECTIVE)**
Given the "sinister" nature of the hemorrhagic pleural effusion and the suspicion of malignancy or disseminated TB, this could represent **leukocytoclastic vasculitis**. The lesions appear somewhat "palpable" in the image, which is a hallmark of small-vessel vasculitis.
**4. SEVERE HYPOALBUMINEMIA & SKIN FRAGILITY**
With a serum albumin of **2.0 g/dL**, the patient has profound anasarca. The skin is chronically stretched and "thin," making it highly susceptible to **senile/solar purpura** or trauma-induced capillary leak from even minor friction against bed linens.
**💡 CLINICAL NOTE**
The presence of purpuric lesions in a patient on ATT warrants a close watch for **drug-induced thrombocytopenia**. If these lesions become bullous or involve mucous membranes, it could signal a more severe reaction like Stevens-Johnson Syndrome (SJS), though the current morphology is more suggestive of a vasculitic or uremic process.
**RECORDS & LINKS**
📋 **69M Pedal edema abdominal distension CCF CKD DM2 Htn metabolic syn WB PaJR**
🔗 **Medication Log (HRZE):** https://vault.pajrhealth.org/
[18/04, 10:08]hu2: 👆@hu1 if you look at this AI input from a differential diagnosis point of view for a human agent unfazed by the AI TLDR it's a welcome balm for it's human cognition but at the same time the human agent doesn't really need to take any action currently on any of the above mentioned differentials other than what is already a part of the patient's current workflow
[18/04, 10:20]hu1: Hmm.. at what point will any action by taken deviating from the current workflow?
Isn't that exactly what the diff diagnosis provides - potential causes and what-if scenarios with cutoff/ trigger points to act on?
eg If the lesions 'become more bullous involve mucus membranes' wouldn't we explore the possibility of SJS?
At this point human agents are also doing dd in their mind and coming to the same conclusion that it is likely just skin fragility and ignoring it. But if any of the what-if next steps germinate from any of the scenarios, human agent will act on it.
Please do correct my understanding. I may not be interpreting value of dd correctly.
[18/04, 10:21]hu2: Yes that's because all the incriminated drugs have already been stopped nearly a week back
[18/04, 10:37]hu1: Isn't the dd given by the bot accounting for that?
My fundamental doubt is - why isn't dd as important as diagnosis. Diagnosis only happens after a series of evolving dd, no?
In this case all steps towards cause finding are dd only, no?
And if AI gets any of that wrong, that's not acceptable. That's what the earlier paper was saying.
[18/04, 10:44]hu2: Because in the end we have to go by just one diagnosis while DD is a host of different pathways.
Diagnosis is more important as it's the endpoint and an expert generally optimises through fast system 1 thinking by homing in on the most likely DD and then only after exhausting that does she even need to consider or think and learn more about the next DD (often never) and while for learners it's good to consider everything, for the system 1 expert those are quite redundant?
[18/04, 11:17]hu1: Aah, so not get into research mode but get into diagnosis mode. Got it.
[18/04, 11:25]hu2: Yes perhaps again boils down to system 1 vs system 2
The same quandary around the utility of addressing different differential diagnostic pathways creeps into this discussion from another PaJR archived here:
[18/04, 10:26]hu2: @PaJR Health Bot Feb 26 going by the images of the skin lesions and the prior history of exposure what is your take on the possible diagnosis?
[18/04, 10:26] PaJR Health Bot Feb 26: Based on the clinical morphology and the timeline of exposure, here is the diagnostic breakdown:
**PRIMARY DIFFERENTIAL: MOLLUSCUM CONTAGIOSUM**
📋 **Case Title:** 18F Bumps WB PaJR
🔹 **Morphology:** The images show multiple 1–3 mm, pearly-white, dome-shaped papules. Crucially, several lesions exhibit **central umbilication** (a tiny dimple in the center), which is a hallmark sign of the *Molluscum contagiosum* virus (MCV).
🔹 **Exposure:** Onset occurred on April 10th, exactly one day after starting swimming in a public pool (April 9th). While the incubation period for Molluscum is typically 2–7 weeks, public pools and shared towels are classic vectors for transmission.
🔹 **Pruritus:** While Molluscum is often asymptomatic, it can cause "Molluscum dermatitis"—an eczematous reaction around the bumps that leads to the intense itching reported.
**SECONDARY CONSIDERATIONS**
1. **Chlorine-Induced Folliculitis / Irritant Dermatitis:** The rapid onset (24 hours post-swim) and intense itching are highly suggestive of a reaction to pool chemicals. However, the "pearly" and "umbilicated" nature of the bumps seen in the images is more specific to a viral etiology than simple irritation.
2. **Tinea Corporis (Fungal):** Given the household exposure (mother has tinea), this must be considered. However, the lesions lack the classic "ring-worm" annular scaling and instead present as discrete papules.
💡 **CLINICAL NOTE**
The patient reports the lesions are "spreading every day." This is likely due to **autoinoculation**—scratching the itchy lesions and then touching other areas of the skin, which spreads the virus.
**RECOMMENDATIONS**
* **Avoid Scratching:** To prevent further spread, the patient should avoid picking or scratching the bumps.
* **Hygiene:** Do not share towels, razors, or personal items with others in the household.
* **Evaluation:** A formal dermatological review is advised to confirm the diagnosis, especially if the lesions continue to spread rapidly or show signs of secondary bacterial infection (pus or increasing redness).
🔗 **Ref Media:**
1. [Antecubital Papules](https://vault.
2. [Forearm Distribution](https://vault.
[18/04, 10:30]hu2: 👆@hu1 there's only one differential that can be tackled with a fungal smear although false positives can be a confounder?
[18/04, 10:31]hu1: In this case, wouldn't we go on as usual until 'if the lesions continue to spread rapidly or show signs of secondary bacterial infection (pus or increasing redness).'. If that happens won't a formal dermatological review be recommended?
False positives are characteristic risk of any test. But given that 1% probability of false positive, doesn't mean that we don't act on the 99% probability that it is indeed that cause, no?
[18/04, 11:04] hu2 : Yes and hence we wait with reassurance till the patient's illness evolves to a point beyond that, which is when we explore the first differential with further testing but by that time even our differentials and order of priority would have changed as the lesions would have evolved?
[18/04, 11:23]hu2: While the medicine trained members (including the engineers) in the PaJR team will have their thoughts around all the above suggestions, as part of our medical cognition inquiry that digs deep into both physician's as well as patient cognition, @hu7, I am really interested to know what are the patient's advocate's thoughts when processing the load of information presented above! @hu8
[18/04, 11:52]hu7: From the patient's pov, I believe it is important to find the root cause of the disease rather than "wait and watch". Infact, waiting will delay treatment thus further leading to complications and worsening the prognosis.
As a physician, we should stick to treating the most likely diagnosis: molluscum contagiousum/dermatitis, while following the precautions and recommendations listed above. In the mean time, it is important to keep the other dds in mind, by ruling out any fungal skin infectious.
I'd like to point out two things here:
1. Just like viral infections, fungal infections also rapidly spread.
2. Not all tinea present as a classic "ringworm" annular pattern. For example: the atypical varients of tinea corporis. (Eczematous plaques). We must also ask if there is any previous h/o tinea or any other skin condition in the patient, and whether the patient was compliant to the medication.
Therefore we must rule out any fungal or irritant etiology, alongside treating the condition as molluscum contagiousum.
[18/04, 12:01]hu8: It is a little bit difficult to understand some of the medical terms .
It would be helpful if you could simplify the main points , if not then I will just google.
Also I am keen to know what are causing the bumps and what l should do to prevent it.
[18/04, 13:31]hu2: Do Google and let us know your understanding of what's wrong with your patient
[18/04, 15:26]hu7: How well does AI give a list of possible dds compared to giving one final diagnosis, and how does this affect the treatment it chooses? This could be a very interesting research question 💡
[18/04, 21:01]hu2: Perhaps needs to be rephrased in the background of the problem statement:
Progressive AI usage and dependence in user driven clinical decision making ecosystems can have potentially harmful consequences and one of the recent papers from Boston here:https://jamanetwork.com/
More here: https://userdrivenhealthcare.
Research question:
Can we review and identify clinical decision making pathways in our shared human and AI agentic platform aka PaJR (500 records) here: https://publications.
to analyse if when how human agents usage of AI has provided optimal system 2 support to their medical cognition, particularly how many times have they used AI assisted differential diagnosis?
