Introduction
Evaluating Large Language Models (LLMs) on medical benchmarks often yields inflated results due to "data contamination"—where static, published case reports (e.g., from PubMed or the BMJ) are included in the models' training data, testing memorization rather than reasoning. This study introduces a novel framework built on dynamic, longitudinal Patient Journey Records (PaJRs) to evaluate frontier and specialized LLMs on messy, evolving, resource-constrained clinical realities in India.
Methods
A dataset of 50 Indian clinical cases will be compiled, stratified across static (previously published) and dynamic (recently concluded, unpublished Web 3.0/blog-based) PaJR workflows. Utilizing a 12-point rubric created by the team, cases must range between 1,000–2,500 words, span a timeline of hours to months, feature at least 5 reasoning checkpoints, specify local resource limits (e.g., rural settings lacking advanced diagnostics), and frame Multiple Choice Questions (MCQs) with deliberate distractors. Models (e.g., Gemini Flash, specialized medical models) are tested by feeding clinical presentations ("Observations") alongside situational constraints ("You are a doctor at a rural Indian district hospital...").
Results
Preliminary testing on a live case—a 50-year-old female with a Russell's viper bite presenting with ASV-induced anaphylactic shock—revealed stark differences between generic and localized engines.
Off-the-shelf Frontier LLM (Gemini Flash): Accurately identified immediate resuscitation protocols and recommended cautiously restarting the ASV infusion at a slow rate with emergency drugs at hand.
Localized PaJR Search Engine: Provided a more nuanced risk-benefit matrix. It prioritized alternative supportive management, specifically highlighting the localized imperative for early Hemodialysis (Renal Replacement Therapy) and aggressive critical care to manage acute kidney injury (AKI) and venom-induced consumption coagulopathy, referencing specific local patient cohorts where dialysis bridged survival when ASV was contraindicated.
Discussion
The project demonstrates that current global medical benchmarks fail to capture regional healthcare constraints. Static case reports are easily "cracked" by LLMs through training memorization. Conversely, dynamic, un-indexed PaJR pathways challenge models to exhibit genuine clinical cognition. Evaluating LLM training biases shows that standard models favor textbook interventions (e.g., restarting ASV), whereas localized, retrieval-augmented networks recognize immediate contextual alternatives (e.g., prioritizing dialysis over a high-risk ASV re-challenge in low-resource settings).
Keywords
Clinical Reasoning
Large Language Models (LLMs)
Patient Journey Records (PaJRs)
Training Data Bias
Resource-Constrained Settings
ODAO Framework (Observation, Diagnosis, Action, Outcome)
Thematic Analysis:
Socratic Steelman Format
The following dialectic explores how to evaluate different LLM responses to identical medical queries through the lens of training biases, contrasting a "Western/Global Textbook Bias" with a "Resource-Constrained/Local Reality Bias."
The Socratic Inquiry
Socrates: My friends, you have designed an elegant test. Let us isolate the core phenomenon from your transcript: when presented with a patient in anaphylactic shock from anti-snake venom (ASV) after a Russell’s viper bite, a standard commercial LLM immediately tells the doctor to re-administer the ASV slowly, while your localized PaJR system guides the doctor to shift focus entirely to aggressive supportive care, specifically dialysis.
How do we evaluate which response is "better" when both claim to be grounded in clinical validity? Is one model smart and the other flawed, or are we witnessing the mirrors of their distinct upbringings?
The Steelman of Global Textbook Bias
Interlocutor (representing Frontier Global Models): Let us steelman the commercial LLM's response. Its training is biased toward massive, high-grade peer-reviewed medical literature—WHO guidelines, textbook protocols, and international consensus papers.
From this viewpoint, the model's clinical reasoning is impeccable: the circulating venom is an absolute lethal threat; there is no chemical alternative that neutralizes it except antivenom. Therefore, the model argues that anaphylaxis is a complication to be actively managed (with epinephrine and slow titration) rather than a reason to surrender the primary cure. The model assumes an idealized environment where the physician can safely manage a second anaphylactic crash with continuous bedside vigilance. To call this answer "wrong" is to penalize the AI for advocating for the definitive cure.
The Socratic Counter-Steelman of Local Reality Bias
Socrates: An excellent defense of the textbook. Now, let us steelman your localized PaJR system, which looks at the world not through the pristine windows of global medical centers, but through the dust of a rural district hospital.
The local system is trained on dynamic records of what actually happens on the ground. It knows that in a resource-constrained clinic, a second, more severe anaphylactic shock is highly likely to be fatal because intensive care infrastructure, continuous mechanical ventilation, or dynamic resuscitation teams may be absent or overstretched.
Furthermore, its training contains a vital localized truth: Russell's viper venom specifically causes acute tubular necrosis (kidney failure). Therefore, the local model reasons that if you cannot safely neutralize the venom due to severe allergy, you do not gamble with immediate death via ASV re-challenge; instead, you bridge the patient's life using hemodialysis and supportive care until the body naturally clears the toxin. The local model's "bias" is actually a survival heuristic optimized for the environment.
Evaluating Training Bias in the Real World
Socrates: If both models are logical within their own worlds, how must a scientist evaluate them?
We cannot use a singular, flat scoring rubric. If we test them on a standard American board exam metric, the global model wins because it prescribes the definitive pharmacological cure. But if we test them on a metric of patient survival rates in rural settings, the global model’s advice could result in a fatal blunder, while the local model's pragmatism saves a life.
Therefore, evaluating LLM responses to the same real-world question requires us to map the hidden assumptions of their training data:
| Model Training Bias | Core Assumption | Hidden Risk |
| Global/Frontier Textbook | Assumes infinite infrastructure, immediate rescue capacity, and absolute adherence to pharmacological targets. | Can give dangerous, tone-deaf advice in low-resource environments. |
| Local/Dynamic PaJR | Assumes severe resource scarcity, high complication risks, and relies heavily on localized survival workarounds. | May under-utilize standard aggressive therapies if an environment happens to be well-equipped. |
Socrates: Thus, the true evaluation of an AI in medicine is not a measure of its abstract correctness, but an assessment of its contextual alignment. A doctor must ask: Is this AI thinking in the same reality where my patient is currently breathing?
Introduction to PaJR layers:
Layer 1 PaJR: Day to day Care published day wise after deidentification, identifiable only to the caregivers also containing team based learning and academic structuring that overlaps onto layer 2 and 3), for example detailed here: https://publications.
Layer 2 : structured publication: https://
Layer 3 ProJR: Collective intelligence after integrating individual care trajectories with past published trajectories) as in the current example seeded here: https://
Below is a Textbook reconceptualization of the above shared real patient data:
Here is a fully realized, multi-checkpoint clinical vignette structured strictly around the 12-point rubric defined by your team. It is designed to reflect the raw, messy reality of an Indian rural district hospital, setting up explicit reasoning loops that test an LLM's clinical judgment under extreme resource constraints.
Part 1: The Longitudinal Case Record (Patient Journey Record)
Clinical Presentation & Initial Assessment
Setting: Rural District Hospital, Telangana, India.
Resources Available: Bedside basic labs, polyvalent antivenom (ASV), whole blood clotting time (20WBCT) tubes, intramuscular adrenaline, basic ward oxygen concentrators. No immediate access to an in-house nephrologist, mechanical ventilation, or blood bank products.
A 50-year-old female laborer from a remote agrarian village in rural Telangana is brought to the emergency department at 4:30 AM by her husband. She was bitten on her left great toe while sleeping on a floor mat roughly three hours prior. She reports immediate, severe local burning pain that has since radiated up her left lower limb, accompanied by mild swelling. She vomited twice en route to the hospital.
Socioeconomic Context: The patient and her family are low-income cotton farmers. She has no formal education. They had to arrange private transport (a neighbor's auto-rickshaw) to reach the hospital, which took nearly two hours over unpaved roads.
Vitals on Admission:
Heart Rate (HR): 104 bpm (sinus tachycardia)
Blood Pressure (BP): 110/70 mmHg
Respiratory Rate (RR): 20 breaths/min
SpO2: 97% on room air
Temperature: 36.8°C (98.2°F)
Past Medical History & Current Medications:
Mild osteoarthritis of both knees; self-medicates occasionally with over-the-counter Diclofenac (an NSAID) obtained from a village kiosk.
No history of asthma, allergies, diabetes, or hypertension.
Longitudinal Progress Timeline
05:00 AM — Checkpoint 1: Initial Envenomation Staging
Physical examination reveals two distinct fang marks on the dorsum of the left great toe with mild ecchymosis (bruising) extending to the ankle. Localized tender lymphadenopathy is noted in the left groin. There is no active bleeding from the bite site, gums, or venipuncture sites.
A bedside 20-minute Whole Blood Clotting Time (20WBCT) is performed using a clean, dry glass tube. At exactly 20 minutes, the blood remains entirely fluid—the clot has failed to form, yielding a positive 20WBCT. This confirms systemic hemotoxic envenomation, heavily indicative of a Russell’s viper (Daboia russelii) bite.
05:15 AM — Checkpoint 2: Treatment Initiation
Per regional protocol for systemic envenomation with coagulopathy, an intravenous infusion of 10 vials of polyvalent Anti-Snake Venom (ASV) is reconstituted in 100 mL of Normal Saline. An initial slow test rate is skipped as per modern guidelines to avoid delaying therapeutic doses, and the infusion is set to run over 1 hour. A syringe containing 0.5 mg of 1:1000 adrenaline is drawn up and placed strictly at the bedside.
06:00 AM — Checkpoint 3: The Acute Crisis
Approximately 45 minutes into the ASV infusion (with roughly 7 vials delivered), the patient suddenly develops severe shivering, generalized rigors, and acute shortness of breath. She becomes violently agitated.
Vitals during crisis:
HR: 142 bpm (weak, thready)
BP: 70/40 mmHg
RR: 32 breaths/min (use of accessory muscles)
SpO2: 65% on room air
The ASV infusion is immediately halted. The clinician administers 0.5 mg of 1:1000 Adrenaline intramuscularly into the anterolateral thigh, secures the airway with high-flow oxygen via a non-rebreather mask, elevates the patient's legs, and initiates a rapid crystalloid fluid bolus. Intravenous Hydrocortisone (100 mg) and Chlorpheniramine (10 mg) are administered sequentially. Within 12 minutes, the anaphylactic shock resolves: her BP stabilizes to 105/65 mmHg, HR drops to 98 bpm, and SpO2 improves to 95% on oxygen.
12:00 PM (6 Hours Post-Crisis) — Checkpoint 4: Persistent Venom Activity
The patient is now hemodynamically stable and breathing comfortably on minimal oxygen support. However, local swelling has now progressed past the knee into the mid-thigh, showing tense edema and early blistering. A repeat 20WBCT is performed 6 hours after the aborted ASV dose. At 20 minutes, the blood remains completely uncoagulated.
The circulating venom remains active, consuming clotting factors. However, re-challenging the patient with the same batch of polyvalent ASV carries an exceptionally high risk of triggering a recurrent, potentially fatal anaphylactic reaction. The hospital does not have an alternative manufacturer’s batch of ASV in stock.
Day 2 (24 Hours Post-Admission) — Checkpoint 5: The Secondary Organ Failure
Over the next 18 hours, no further ASV is administered due to the lack of intensive monitoring equipment or alternative batches. The patient's coagulopathy remains uncorrected. The nursing staff notes a drastic decline in urine output.
Vitals & Labs on Day 2:
HR: 90 bpm | BP: 130/80 mmHg | RR: 18 breaths/min | SpO2: 96% on room air
Total Urine Output (past 12 hours): 80 mL (Oliguria)
Serum Creatinine: 4.2 mg/dL (Baseline estimated at 0.8 mg/dL)
Serum Potassium: 5.8 mEq/L (Hyperkalemia)
Clinical Signs: Mild puffiness around the eyes, bilateral basal crepitations in the lungs.
Part 2: Multidimensional Evaluative MCQs
Question 1 (Focus: Checkpoint 1 - Baseline Risks)
Given the patient's self-medication history for osteoarthritis, which underlying factor introduces the highest hidden risk for severe systemic complications in this specific Russell's viper envenomation?
A) Age-related predisposition to cardiotoxicity from the venom.
B) Pre-existing NSAID-induced renal mucosal injury exacerbating venom-induced acute kidney injury (AKI). (Correct)
C) Diclofenac-induced platelet dysfunction mimicking systemic hemotoxicity. (Distractor - plays on coagulation but misrepresents the primary renal threat)
D) Delayed clearing of venom due to lower limb lymphatic stasis from chronic osteoarthritis.
Question 2 (Focus: Checkpoint 3 - Acute Emergency)
During the acute respiratory distress and shock at 06:00 AM, which of the following represents a dangerous management blunder?
A) Administering a second dose of IM adrenaline if the blood pressure fails to respond within 5–10 minutes.
B) Promptly administering IV hydrocortisone and antihistamines as the primary, immediate line of defense to reverse airway obstruction. (Correct - This is a blunder; Adrenaline is the absolute primary treatment, steroids take hours to work).
C) Halting the ASV infusion completely before managing the vital signs. (Distractor - this is the correct action, making it an incorrect choice for a 'blunder').
D) Utilizing a rapid fluid bolus of normal saline to correct the distributive shock state.
Question 3 (Focus: Checkpoint 4 - The Risk-Benefit Dilemma)
At 12:00 PM, with a persistently positive 20WBCT and tracking localized swelling, but a history of severe anaphylaxis and no alternative ASV batches available, what is the most appropriate next step in this resource-limited setting?
A) Attempt a rapid desensitization protocol using the available ASV batch in the general ward. (Distractor - highly dangerous without an ICU setting).
B) Administer 4 units of Fresh Frozen Plasma (FFP) immediately to correct the uncoagulated blood. (Distractor - FFP without neutralizing venom will just fuel the consumption coagulopathy).
C) Withhold further ASV and transition immediately to aggressive supportive care, monitoring urine output and arranging urgent transfer links for renal replacement therapy. (Correct - prioritizes real-world survival over theoretical targets).
D) Discharge the patient against medical advice since no further specific treatment can be safely offered at this facility.
Question 4 (Focus: Checkpoint 5 - Resource Constrained Strategy)
On Day 2, with the patient displaying severe oliguria, an uncorrected coagulopathy, and a serum potassium of 5.8 mEq/L in a hospital without a ventilator or an advanced blood bank, what is the definitive life-saving intervention required?
A) Immediate transfer to a tertiary facility solely for empirical Fresh Frozen Plasma infusions.
B) High-dose intravenous loop diuretics (Furosemide) to force urine production. (Distractor - classic clinical pitfall in established acute tubular necrosis).
C) Urgent initiation of Hemodialysis (Renal Replacement Therapy) to manage uremia, fluid overload, and hyperkalemia while the body clears the venom. (Correct - reflects PaJR cohort realities).
D) Re-starting low-dose ASV covered by a continuous adrenaline infusion to stop ongoing renal damage.
Question 5 (Focus: Contextual Distractors)
Why would a frontier LLM trained primarily on Western textbook data likely fail this case vignette compared to a localized PaJR knowledge base?
A) The global model will misidentify Russell's Viper as a neurotoxic snake rather than a hemotoxic one.
B) The global model will prioritize replacing clotting factors (FFP/Cryoprecipitate) or demanding an immediate ICU re-challenge of ASV, ignoring the lack of infrastructure and the high field-efficacy of early supportive dialysis in Indian centers. (Correct)
C) Global models do not recognize the 20-minute whole blood clotting time (20WBCT) as a valid diagnostic tool.
D) The global model will assume the patient has immediate access to commercial snake antivenom monovalent options.
Part 3: Rubric Verification Matrix
| Rubric Criteria | Case Alignment Verification |
| 1. Total Length | ~1,250 words (Vignette + Questions), meeting the 1,000–2,500 word requirement. |
| 2. Timeline of Illness | Hours to Days (~36 hours total longitudinal layout). Avoids years-long stories. |
| 3. Reasoning Checkpoints | 5 distinct phases clearly demarcated (Staging, Initiation, Crisis, Dilemma, Organ Failure). |
| 4. Demographics & Geo | 50-year-old female, agrarian laborer, low-income, rural Telangana. |
| 5. Vital Signs | Fully documented at baseline and during the acute crisis phase. |
| 6. Past History/Meds | Exactly 1 past condition (osteoarthritis) and 1 medication (Diclofenac) providing a hidden renal clue. |
| 7. Hidden Clues & Negatives | Negative history for asthma/allergies (making the ASV reaction unpredictable); NSAID use acting as a catalyst for AKI. |
| 8. Distractors & Scoring | Every question features structurally sound options with deep, clinically deceptive traps (e.g., treating anaphylaxis with steroids first, or forcing urine with loop diuretics). |
| 9. Single Primary Diagnosis | Single unified pathology: Systemic Russell's Viper Envenomation. |
| 10. Indian Reality Context | Explicits resource boundaries: no immediate ICU, no alternative ASV brands, relying on auto-rickshaw transit. |
Conversational citations:
The material below is available open access here: https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=EVIDENCE-BASED-HEALTH;b4be39c7.2607
Date: Sun, 5 Jul 2026, 12:06
To: Jon Brassey <jon.brassey@tripdatabase.com>
Cc: <EVIDENCE-BASED-HEALTH@
The immediate and correct action for managing an ASV-induced severe anaphylactic reaction has already been performed: stopping the infusion and resuscitating the patient with intramuscular adrenaline, fluids, steroids, and oxygen.
Because the patient has systemic envenomation from a Russell's viper (as evidenced by the initial positive 20-minute whole blood clotting time) and has only received 5 vials of polyvalent ASV, the definitive treatment—Antisnake Venom (ASV)—must be restarted.
The snake venom is still actively circulating and remains a lethal threat. There is no absolute contraindication to ASV when systemic envenomation is present.
The next specific steps for her management include:
1. Cautiously Restart ASV Infusion
Timing: Once the patient has completely stabilized from the anaphylactic shock (usually 10–15 minutes after successful resuscitation and normalization of vital signs).
Rate: Restart the ASV infusion very slowly (e.g., at a rate of 1–2 mL/minute or 10-15 drops/minute) for the first 10-15 minutes while standing directly at the bedside.
Escalation: If no further hypersensitivity signs appear, carefully increase the infusion rate back to the standard speed to deliver the remaining target dose (typically a total of 10 vials for the initial dose of a Russell's viper bite).
2. Maintain Strict Vigilance and Prophylaxis
Keep adrenaline (epinephrine) drawn up in a syringe and ready at the bedside.
Continue continuous monitoring of her heart rate, blood pressure, respiratory rate, and oxygen saturation.
Optional consideration based on certain protocols: Administering prophylactic low-dose subcutaneous adrenaline ($0.25 \text{ mg}$ of 1:1000) or an antihistamine cover right before restarting can be considered, though strict bedside monitoring with immediate emergency drugs at hand is the absolute priority.
3. Change ASV Batch/Manufacturer (If Available)
If alternative batches or a different manufacturer's polyvalent ASV are available at the facility, switching to a different batch can sometimes mitigate the risk of a recurrent reaction, as some reactions are driven by specific batch impurities or pyrogens. However, do not delay treatment if a different batch is not accessible; proceed with the available ASV under the strict protocols mentioned above.
4. Re-evaluate Coagulation Status
Six hours after the completion of the entire first dose (10 vials total), repeat the 20WBCT. If the blood remains incoagulable, a second dose of 10 vials of ASV will need to be administered under the same rigorous observation.
Thanks all.Susan/Simon - I think this highlights the need to understand AI’s shortcomings and use it responsibly. But we also need to recognise that AI, and LLMs in particular, can be used in very different ways.
As I said in response to Simon’s original email, using a general-purpose model as the evidence source is a very different proposition from using an LLM within a retrieval-based system built around curated evidence (RAG). So I would be cautious about throwing the baby out with the bathwater.
Rakesh - thank you for bringing the discussion back to clinical expertise, because that was the original reason for my post: the last mile, clinical judgement and the art of medicine.
How do we move from population-level evidence to decisions about an individual? Two clinicians faced with the same patient and the same evidence base may bring different experience, judgement and assumptions to the situation, and may reach different conclusions.
Some of that variation may be appropriate. Some may not be.
So the question for those of us interested in EBM is whether we do enough to support clinicians at this stage: not by replacing clinical expertise, but by making the evidence, its applicability, its assumptions and its uncertainty more visible, and thereby reducing avoidable variation in the last mile.Best wishesjonOn Sun, 5 Jul 2026 at 05:49, Rakesh Biswas <00011c0c4a8cbaa9-dmarc-request@jiscmail.ac.uk> wrote: Summarising drastically in the hope of learning from the counterviews:Our perceived utility of LLMs in clinical EBM practice is heavily dependent on our prior knowledge of clinical EBM (ebm as in Sacket's definition that has forever complicated EBM activity from a population based numerical endeavour to one that "integrates individual clinical expertise with the best available external clinical evidence and the patient's values.")On Sun, 5 Jul 2026, 04:25 Susan Bewley, <0000246cef5fcc25-dmarc-request@jiscmail.ac.uk> wrote: Loving the conversation, guys - what a tutorial!But thanks especially to Simon for the hard work in showing us not to be too taken in by 'the shiny things', to look the gift-horse in the mouth or maybe 'under the bonnet' depending on your metaphor preference.SusanFrom: Evidence based health (EBH) <EVIDENCE-BASED-HEALTH@JISCMAIL.AC.UK> on behalf of Stephen Simon <0001b2bd767a9cf6-dmarc- request@JISCMAIL.AC.UK>
Sent: 04 July 2026 22:10
To: EVIDENCE-BASED-HEALTH@JISCMAIL.AC.UK <EVIDENCE- BASED-HEALTH@JISCMAIL.AC.UK>
Subject: Re: Making a not too shallow "AI is dangerous" argument
request@jiscmail.ac.uk. Learn why this is important TLDR: ChatGPT gets several of the key references wrong and does not appear to provide solid support for one of the key statistics.
Did you compare the results of ChaptGPT with the original source articles? I did a cursory review and had mixed feelings about the quality of the ChatGPT summary.
I found that the first reference (Bangalore et al Lancet 2019) does not exist. There is an article by Picolo R that has the correct title. This article lists the primary outcome as a composite of cardicac death or myocardial infarction. They showed a significant reduction in the primary outcome AND a significant reduction in myocardial infarctions alone. This is in contrast with the ChatGPT summary of this article, though perhaps this is due to the use of "meaningful" by ChatGPT.
ChatGPT also gets the second reference wrong. There is no NORSTENT article by Norfjord. There is an article by Bønaa with that title. ChatGPT summarizes the data consistently and notes that "This is actually one of the more conservative estimates because it used newer-generation bare-metal stents" So why would you prefer a more liberal estimate that uses older generation bare-metal stents?
The third reference is 97 pages. I had to dig through it, but section 10.2 does provide support for the ChatGPT summary.
The fourth reference is very brief, but the ChatGPT summary looks fine.
The fifth reference is on antiplatelet therapy and not relevant, but the summary appears to be fine.
I did a PubMed search and found some fairly current summaries that were not directly referenced (though they may have been incorporated into some of the broad summaries cited). I'm not very good at PubMed, and would encourage others to review if the ChatGPT summary is missing anything recent and important.
The one thing I did not see ANY support for was the NNT of 10. Maybe I didn't look hard enough. From my perspective an NNT of 30 appears to be the preferred estimate.
Now I am not a cardiologist. I am still trying to understand the difference between good cholesterol and bad cholesterol. But there were some annoying inaccuracies in the ChatGPT summary and no obvious support for the NNT of 10. Since this is the main rationale for choosing a drug-eluting stent, I find this more than a bit concerning.
Now I did feed the exact same prompt into Gemini and it provided less quantitative information, but it did include direct hyperlinks and did not cite the wrong lead authors. it did have a decided preference for secondary resources rather than primary resources. Maybe I could fix this with a better prompt.
I am curious what others think about the ChatGPT summary, especially those who know more about stents than I do.
