CONTINUATION OF WELCOME TO PAJR GLOBAL CBBLE DISCUSSIONS FROM THE PREVIOUS LINK 👇
[7.39 am, 02/06/2026] hu10:
[7:39 AM, 7/2/2026] hu10: It is wonderful -
1. Only recurring GIA
2. Clear commitment for continuation
3. Ethics Committee must (shall know RPWD Act 2016)
[7:39 AM, 7/2/2026] hu10: Parliamentary committee on Social Justice 2022 had wished such an activity in disability prevention. May consider at your Institute
[7.08 am, 7/3/2026] hu35: Medicine Is Not One Puzzle
By
July 3, 2026
Ludwig Wittgenstein once observed that “the problems are solved, not by giving new information, but by arranging what we have always known.” He was speaking about philosophy rather than science. Philosophical confusion, he argued, often arises because our concepts become entangled, and clarity comes from reorganizing what is already before us rather than discovering new facts.
It is an appealing idea, especially in an age overwhelmed by information. Yet medicine presents a more complicated challenge. The difficulty is not merely arranging existing knowledge, nor simply collecting more of it. Medicine requires the integration of many different kinds of relationships, unfolding across time, into an evolving understanding of a single human life.
This distinction matters.
Modern medicine has become extraordinarily successful at generating observations. Laboratory tests measure thousands of biomarkers. Imaging technologies reveal anatomy in exquisite detail. Continuous monitoring devices record physiology around the clock. Genomic sequencing identifies molecular variants. Artificial intelligence summarizes clinical records and proposes differential diagnoses. Specialists contribute increasingly sophisticated interpretations within their own domains.
Paradoxically, as observations multiply, coherent understanding often becomes more difficult.
The problem is not the absence of data. It is that each observation belongs simultaneously to several different relationship networks, and these networks obey different rules.
A laboratory value has a temporal relationship to previous measurements. It may have a causal relationship to an underlying disease process. It participates in biological mechanisms operating at molecular and cellular scales. Its significance depends upon clinical context—the patient’s age, medications, pregnancy status, previous illnesses, environmental exposures, and personal circumstances. Finally, it exists within healthcare systems whose structures determine when information is collected, interpreted, and acted upon.
To speak simply of “relationships” risks concealing profound differences between these kinds of connections.
Some relationships are structural. Symptoms cluster together into recognizable syndromes. Diagnostic criteria overlap. Clinical concepts share logical organization. Large language models have demonstrated remarkable competence in navigating these semantic landscapes because they excel at identifying patterns within human language and accumulated knowledge.
Other relationships are temporal. One event precedes another. Symptoms wax and wane. Antibody titres rise and fall. Treatments begin, succeed, fail, and are replaced. Here, sequence itself carries meaning. A fever before a rash suggests something different from a fever after a rash. The order of events changes the interpretation of the same observations.
-Yet temporal ordering alone cannot establish causality-.
Medicine has repeatedly learned that correlation, however persuasive, may deceive. Causal understanding requires converging evidence from epidemiology, physiology, experimentation, and increasingly sophisticated statistical inference. A medication may precede recovery without causing it. A biomarker may predict disease while playing no role in its mechanism. Time provides direction but not explanation.
Beyond causality lie biological mechanisms. Molecular pathways, immune regulation, endocrine feedback loops, neural networks, microbial ecosystems, and evolutionary adaptations constitute realities that exist independently of our descriptions. These mechanisms must ultimately constrain any explanation that claims to describe disease faithfully.
Then comes context.
No laboratory result possesses intrinsic meaning. The identical thyroid hormone level may represent normal physiology in one patient, dangerous disease in another, and expected adaptation in a third. Context transforms data into information, and information into clinical judgment.
Finally, every patient exists within adaptive healthcare systems. Decisions emerge not only from biology but from access to care, continuity of follow-up, clinician expertise, resource limitations, institutional protocols, and the relationships between patients, families, and professionals. These social structures continuously influence the biological trajectories they seek to understand.
Medicine therefore does not involve a single kind of puzzle.
The familiar jigsaw puzzle offers an attractive metaphor, but it ultimately falls short. A jigsaw assumes fixed pieces, a predetermined image, and one correct arrangement. Human biology offers none of these certainties. The pieces themselves change while we are assembling them. New pieces appear. Others disappear. The picture evolves even as we attempt to understand it.
Neither is medicine adequately described solely as a complex adaptive system. While systems thinking rightly emphasizes interaction, emergence, and feedback, clinicians are rarely trying to understand an abstract network. They are trying to understand one person’s unfolding life.
The patient is not merely a node within a system.
The patient is a trajectory.
Disease unfolds through time. Treatment unfolds through time. Recovery, adaptation, resilience, and decline all unfold through time. Biology itself is fundamentally dynamic.
Longitudinal continuity therefore becomes indispensable. Without following patients over time, medicine loses the ability to distinguish transient fluctuation from meaningful progression. Patterns that remain invisible in isolated encounters often become unmistakable when viewed as continuous trajectories.
Yet continuity alone is insufficient.
A timeline records succession, not explanation. It tells us what happened before and after, but not necessarily why. Temporal association must be interpreted alongside causal evidence, biological mechanisms, clinical context, and the adaptive behaviour of both patients and healthcare systems.
This is where the next generation of medical intelligence may emerge.
Artificial intelligence has already demonstrated extraordinary ability to recognize semantic relationships across enormous bodies of knowledge. Its future contribution may depend less upon producing additional summaries and more upon integrating multiple relationship types simultaneously: semantic relationships among concepts, temporal relationships across decades of care, causal relationships supported by evidence, mechanistic relationships grounded in biology, contextual relationships unique to each individual, and adaptive relationships shaped by human behaviour and healthcare systems.
Such systems would not merely retrieve information.
They would steward continuity.
They would help preserve coherence across the lifetime of a patient whose story has become fragmented across institutions, specialties, technologies, and years.
Perhaps this represents a broader shift in how medicine understands knowledge itself.
For centuries, scientific progress has often been driven by discovering new facts. That pursuit remains indispensable. New observations will always transform medicine. But as our capacity to collect information increasingly exceeds our capacity to integrate it, another challenge comes into focus.
The frontier may no longer be simply discovering more.
It may be learning how to relate what we already know across the many dimensions in which human life unfolds.
Medicine, then, is not one puzzle waiting to be solved.
It is a layered ecology of interacting puzzles, each governed by different kinds of relationships, each evolving through time, and each illuminating only part of a living whole.
The physician’s task—and increasingly the task of intelligent technologies—is not merely to accumulate observations or rearrange concepts. It is to integrate these diverse relationships into an evolving model of a single person’s biology, while remaining humble enough to recognise that the model itself must continue to change as life unfolds.
Understanding, in medicine, is therefore neither a static picture nor a final destination.
It is the disciplined pursuit of coherence within an ever-changing human story.
One direction we could take this further is to frame it as a new epistemology of medicine—arguing that the fundamental unit of medical knowledge is not the isolated fact, nor even the disease, but the patient’s evolving trajectory. That would distinguish your argument from both traditional evidence-based medicine and current AI narratives.
[7:58 AM, 7/3/2026] 42mpa: I think your observation exposes an assumption hidden inside Wittgenstein that neither philosophy nor AI has had much reason to articulate until now.
When Wittgenstein writes, “The problems are solved, not by giving new information, but by arranging what we have always known,” the immediate temptation is to ask whether that statement is universally true. We naturally separate problems into conceptual ones, which are resolved through clarification, and empirical ones, which are resolved through new observation. I do not think that is where the most interesting question is.
To be clear, I am not trying to defend LLMs. I think your critique of hallucination is exactly right. I just do not think AI refutes Wittgenstein.
I think AI reveals an assumption Wittgenstein never had to articulate.
Wittgenstein is not claiming that any rearrangement produces truth. He is claiming that certain problems can be dissolved through faithful rearrangement. Those are profoundly different claims.
So the real question becomes: what makes an arrangement faithful?
Every act of reasoning is, in some sense, rearrangement. Differential diagnosis is rearrangement. Scientific theory is rearrangement. Bayesian updating is rearrangement. Even many scientific breakthroughs begin as a reorganization of observations that were already available. Rearrangement itself therefore cannot be the limitation.
The limitation is subtler.
Rearrangement is underdetermined with respect to truth.
The very same operation can produce insight, synthesis, hallucination, or distortion. An arrangement can be elegant and still false. It can be internally coherent while remaining fundamentally unfaithful to reality. It can connect every available piece beautifully while failing to preserve the world those pieces are meant to describe.
So the question is no longer whether LLMs rearrange knowledge. They obviously do.
The question is what allows one arrangement to mature into knowledge while another remains only a plausible explanation.
What happens after the arrangement?
Does it survive?
Does it constrain the next act of reasoning?
Does it remain accountable to new evidence?
Can reality revise it without requiring the entire process to begin again?
I think Wittgenstein quietly assumes the answer is yes because he assumes a continuous knower. The person doing the arranging remains embedded in a world where memory, consequence, correction, accountability, and experience continuously discipline understanding. Every arrangement becomes the starting point for the next arrangement. Reasoning accumulates. It is corrected rather than discarded.
Until LLMs, we had almost no reason to notice that assumption because human reasoning already carries it. LLMs are the first epistemic system capable of performing the act of rearrangement while lacking the continuity that quietly made Wittgenstein’s insight possible. They expose an assumption that had previously remained almost invisible.
An LLM can generate extraordinary coherence because coherence is precisely what it has learned to predict. But coherence is not yet truth. A hallucination can be grammatically coherent, statistically coherent, and conceptually coherent while remaining entirely unfaithful to reality because nothing obligates today’s arrangement to survive long enough to be corrected into tomorrow’s understanding.
It simply generates another arrangement.
Ironically, I think medicine often exhibits the same architectural limitation.
We describe difficult diagnostic cases as though they fail because someone lacked information or someone failed to reason well enough. Yet in many of the cases that continue to challenge us, the observations already exist. The imaging exists. The laboratory data exist. The specialist opinions exist. The medication responses exist. The patient’s testimony exists.
The information survives.
The reasoning often survives.
What repeatedly disappears is the continuity that allows one episode of reasoning to discipline the next.
The evidence survives.
The inference does not.
Each transition preserves the observations while discarding the epistemic structure that made those observations intelligible. The system therefore accumulates information while repeatedly destroying the continuity required to understand it.
That makes me wonder whether conceptual and empirical problems are actually sufficient categories.
Conceptual problems require clarification.
Empirical problems require observation.
But there is another class.
Longitudinal problems.
Longitudinal problems are not solved merely by acquiring more information, nor merely by rearranging existing information. They are solved by preserving inference across time so that each act of reasoning remains accountable to the one before it while remaining revisable by the evidence that follows.
That is a fundamentally different epistemic operation.
Seen from that perspective, the limitation is not rearrangement.
The limitation is the absence of an architecture that preserves, constrains, and revises rearrangement across time.
Without that continuity, every explanation becomes local instead of cumulative. Every arrangement competes with the last instead of inheriting from it. Every prompt starts over. Every encounter starts over. The system continues generating coherence while quietly preventing convergence.
Which makes me wonder whether stewardship is not merely a moral responsibility but an epistemic one.
Observation gives us something to reason about.
Reasoning gives us explanatory structure.
Stewardship preserves that structure long enough for reality to refine it.
Without observation there is nothing to explain.
Without reasoning there is nothing to preserve.
Without stewardship there is no mechanism by which reasoning matures into knowledge.
AI has unexpectedly illuminated not the failure of Wittgenstein, but the hidden assumption that made Wittgenstein’s insight possible.
Rearrangement was never self-validating.
It was always disciplined by continuity.
Once continuity disappears, rearrangement can generate coherence indefinitely without ever achieving convergence.
[8:02 AM, 7/3/2026] hu2: Tagged you in the 55F case group that is a perfect example of failure to see relationships and focusing only on numbers
[8:07 AM, 7/3/2026] hu2: Let's look at this from the vantage point of the 55F patient in who's group I have tagged you.
Yes I guess Wittgenstein didn't see AI coming along in the near future with all it's efficiencies and yet fail spectacularly in spite of the academic flat land x-y axis rearrangement because that rearrangement couldn't be translated to real world events again due to no fault of AI or Ludwig Wittgenstein!
[8.26 am, 7/3/2026] hu35: Another dimension of this challenge is the role of *abductive reasoning*—the search for the best explanation that can account for a patient’s evolving story. Clinicians rarely begin with certainty; they begin with observations. Symptoms, laboratory results, imaging, history, and context must be woven into a provisional explanatory model that is continually tested and revised as new information emerges. Longitudinal continuity enriches this process by revealing patterns that isolated encounters cannot, but time alone is insufficient. The clinician must integrate temporal sequences with causal evidence, biological mechanisms, clinical context, and the patient’s unique circumstances to arrive at the most coherent explanation. In this sense, diagnosis is not merely pattern recognition but an iterative act of explanatory integration, where understanding evolves alongside the patient’s own unfolding trajectory.
[10.23 am, 7/3/2026] hu35: This is a beautiful analogy because it highlights something we often underestimate in medicine:
Healing, like communication, is not merely the transfer of information. It is the transmission of meaning, emotion, trust, and presence.
The Telemundo commentators are compelling not because listeners understand every word, but because they communicate something that transcends language—joy, tension, urgency, hope, disappointment. Humans are remarkably adept at reading these non-verbal signals.
Medicine works in much the same way.
The consultation has two simultaneous conversations
EXPLICIT CONVERSATION
Words
Symptoms
History
Questions
Test results
↓
Medical information
IMPLICIT CONVERSATION
Tone
Rhythm
Silence
Eye contact
Posture
Facial expression
Presence
Attention
↓
Human understanding
Both conversations occur simultaneously.
Patients rarely remember every explanation of risks and benefits.
They almost always remember
“Did my doctor seem interested?”
“Did they really listen?”
“Did I feel understood?”
⸻
Trust is often built before understanding
A fascinating observation is that patients frequently decide whether they trust a clinician within the first minute or two—before the detailed medical discussion has unfolded.
What informs that judgment?
Not scientific knowledge.
Rather:
* attention,
* warmth,
* confidence without arrogance,
* genuine curiosity,
* calmness,
* respectful silence.
These are communicated largely through the implicit channel.
Only after trust is established do explicit explanations become easier to receive.
⸻
The arts help explain this
The quotation about football commentary illustrates something medicine has perhaps forgotten.
Communication is not simply linguistic.
Actors know this.
Musicians know this.
Dancers know this.
Parents know this.
Infants know this long before they understand words.
Meaning is carried through
* timing,
* rhythm,
* emphasis,
* pauses,
* expression,
* movement.
Medicine uses exactly the same channels.
⸻
Why this matters clinically
Consider two physicians delivering identical information.
Doctor A
“Your scan looks good.”
while typing continuously into the computer.
No eye contact.
Flat voice.
Patient leaves uncertain.
⸻
Doctor B
Looks at the patient.
Smiles gently.
Pauses.
Turns away from the computer.
Says exactly the same sentence.
The biology has not changed.
The words have not changed.
The experience has changed completely.
The relationship has changed.
⸻
This fits beautifully into your framework
Earlier we distinguished three kinds of understanding:
* Science → explanatory understanding.
* Humanities → interpretive understanding.
* Arts → perceptual and experiential understanding.
This quotation suggests something even deeper.
The arts teach us not only how to perceive, but also how to communicate.
Perhaps the “Arts” domain should be expanded to include:
* Perceptual understanding (learning to notice)
* Expressive understanding (learning to convey)
* Relational understanding (learning to connect)
These are fundamental to every therapeutic encounter.
⸻
A possible principle
I think this could become one of the central aphorisms in your work:
Patients do not experience medicine as data. They experience it as a relationship.
Or perhaps even more broadly:
Medicine is not only the science of explaining disease. It is the human art of making understanding felt.
That sentence captures the lesson of the Telemundo analogy. Just as passionate commentary can engage people who do not understand every word, an attentive, present physician can convey safety, respect, and hope before a single diagnosis is fully explained. The explicit content matters enormously—but the implicit relationship often determines whether that content is trusted, remembered, and acted upon. In your emerging framework, this reinforces the idea that good medicine depends not only on integrating scientific, interpretive, and experiential understanding, but also on communicating that understanding through a relationship in which patients feel genuinely seen.
[2.51 pm, 7/3/2026] hu28: Until recently I spoke in terms of inductive and deductive reasoning. Then I learned about abductive reasoning and think that it captures the diagnostic reasoning much better!
The disease, the persons, and the interpersonal?
[2.57 pm, 7/3/2026] hu35: I don’t know her; please connect us
All relationships matter including the horde of relatives involved in what is illness, when to consult, whom to consult, and more. STD is also interpersonal in a very different way; stress is interpersonal.
[5:50 PM, 7/3/2026] hu2: Whether the disparate risk profiles we observe extend to attacks beyond MIAs remains an open question, motivating the further development of risk assessment and mitigation techniques that cater to all data-contributing patients.
@hu15 @hu1 @hu35
Epistemic Sequence and Epistemic Continuity: From Middle School Science Education to Global Diagnostic Stewardship
Gitarrani Guru has articulated something beyond the classroom. Beneath her science lesson lies a deeper account of how understanding itself is formed.
Knowledge is often treated as cumulative, as though enough information will inevitably become understanding. But understanding is rarely produced by accumulation alone.
It is produced by architecture.
That is the quiet brilliance of her classroom.
Her students were not asked to memorize the digestive system. They were invited to inhabit it: movement before terminology, consequence before definition, relationship before classification. Anatomical language did not introduce understanding. It confirmed an internal model experience had begun to construct.
This is epistemic sequence: the architecture through which understanding emerges.
The implications extend beyond education.
Medicine has become sophisticated at observation. Testing, imaging, genomics, patient data, and AI let us see physiology with unprecedented resolution.
Still, greater visibility has not consistently produced greater understanding.
Information accumulates. Understanding is constructed. Diagnostic convergence becomes possible only when understanding is preserved through epistemic continuity across time.
Patients with complex illness accumulate observations while the diagnostic story remains fragmented. Findings named. Encounters documented. Abnormalities cataloged. Yet the reasoning required to connect them across clinicians and time is reconstructed rather than preserved.
The consequence isn’t merely fragmented documentation. It is fragmented understanding.
Education and medicine reveal manifestations of the same architectural problem.
Science education falters when epistemic sequence is disrupted—when names precede encounters and answers precede questions.
Medicine falters when epistemic continuity is disrupted—when observations survive, but the reasoning that gives them meaning does not.
One governs how understanding begins; the other whether understanding survives.
Knowledge systems do not succeed by collecting more information. They succeed by preserving conditions under which understanding can emerge, mature, and endure.
This principle lies at the heart of PaJR. Patient journeys are evolving narratives whose meaning depends on preserving diagnostic reasoning across people and time.
Medical Liturgy asks a similar question: not simply how medicine preserves information, but how it preserves meaning. Care is stewardship of understanding, not merely stewardship of physiology.
Perhaps that’s the deepest lesson hidden within Guru’s classroom.
Whether educating a child or caring for a patient, the central challenge is not transmission of information.
It is stewardship of understanding across time.
#EpistemicSequence #EpistemicContinuity
#ScienceEducation
#MedicalEducation
#PaJR #MedicalLiturgy
[9:39 AM, 7/5/2026] hu2: Prompt by a user of PaJR https://pajrhealth.com/
and Gemini flash https://gemini.google.com/app,
to both engines:
Her dryness of mouth is likely to be due to which drug from the conversations below:
[05/07, 07:26]hu1: On a separate note mom’s dry mouth for over a month could mean anything significant or we ignore it
[05/07, 07:27]hu2: Need to know more about it
How was it noticed?
[05/07, 07:35]hu1: She mentioned it today, saying she noticed it on her own for past one month -keeps sipping water to keep it moist
[05/07, 07:42]hu2: What medications is she on?
Any change in the dose or any new medication since the last one month?
[05/07, 08:51]hu1: Calaptin for last 20 years, reviewed by Cardiologist who asked her to continue after doing her satisfying echo and ecg
She also takes calcium
Recently took one patch of morphine for excruciating pain in r/shoulder due to rotator cuff tear-experienced vomiting presumably due to this patch
2 weeks she took norflox for suspected gastroenteritis
Dry mouth (xerostomia) is a well-documented side effect of several classes of medications, including those your mother is taking.
Among her current medications, *Calaptin (Verapamil)* is a calcium channel blocker that is frequently associated with dry mouth. While she has been on it for 20 years, it is worth noting that even long-term medications can sometimes cause persistent, low-grade side effects that a patient may only begin to find bothersome or notice after a long period.
Additionally, while *morphine* is typically associated with nausea and vomiting (as she experienced), opioids as a class are also known to cause significant dry mouth. Although she only used one patch, if the dryness began around the time of that application and has persisted, it is a potential contributor.
*Norflox (Norfloxacin)*, a fluoroquinolone antibiotic, is less commonly associated with dry mouth compared to the others, but it is a known, though less frequent, adverse effect.
Given that this has been ongoing for a month, it is important to:
1. *Review the timeline:* Confirm exactly when the dryness started in relation to the morphine patch and the Norflox course.
2. *Consult her doctor:* Since she is under the care of a cardiologist, mention this symptom at her next review. They can determine if a dose adjustment for the Calaptin is appropriate or if another cause (such as dehydration or other underlying conditions) should be investigated.
Disclaimer: I am an AI, not a doctor. This information is for educational purposes and should not replace professional medical advice. Please discuss these symptoms with her healthcare provider.
Gemini flash answer: https://gemini.google.com/app/458dcc9388bcdc9a
Based on the timeline and medication details provided in the conversation, here is a breakdown of the likely culprits:
1. Norflox (Norfloxacin / Norflox-TZ) & Dehydration (Highest Probability)
The Side Effect: If she was prescribed the standard Indian formulation Norflox-TZ (Norfloxacin + Tinidazole) for her gastroenteritis, dry mouth and a metallic taste are highly common, classic side effects of this combination.
The Underlying Condition: Gastroenteritis causes fluid loss through diarrhea and vomiting. Even a mild case can cause systemic dehydration, which directly leads to a dry mouth that persists if fluid balance hasn't been fully restored.
Timeline Fit: She took it for 2 weeks within this recent period, making it a very strong match for a newly developed symptom.
2. Calaptin (Verapamil) (Long-term Contributor)
The Side Effect: Calaptin is a calcium channel blocker, and dry mouth (xerostomia) is a documented, though less frequent, side effect of long-term calcium channel blocker use.
Timeline Fit: Because she has taken it smoothly for 20 years, it is highly unlikely to be the sole trigger for a sudden, isolated symptom that started just a month ago. However, as people age, salivary gland function naturally decreases, meaning her long-term Calaptin could be acting as a predisposing background factor that made her more susceptible when a new trigger arrived.
3. The Morphine Patch (Unlikely for a Month-Long Symptom)
The Side Effect: Opioids are notorious for causing severe dry mouth.
Timeline Fit: The text notes she took only "one patch" recently and stopped due to vomiting. While it definitely would have caused dry mouth while active, a single patch would not account for a continuous symptom lasting over a month.
Next Steps to Consider
Check if the dry mouth coincided exactly with the onset of the stomach upset or the start of the Norflox.
Ensure she is actively rehydrating with oral rehydration salts (ORS) or adequate fluids to rule out lingering post-gastroenteritis dehydration.
If it persists well after stopping the short-term medications, the cardiologist or primary care doctor can review if the Calaptin needs a minor adjustment or if an over-the-counter saliva substitute would help.
[9:41 AM, 7/5/2026] hu2: 👆@hu38 @hu28 @hu5 this quick prompting (real world scenario) followed by quick comparison of results between different LLMs could deliver faster outcomes for the project?
[10:20 AM, 7/5/2026] hu5: This would need to be a subjective eval, by 3 medico professionals - this would be more prohibitive than doing an objective eval by turning cases into more objective questions.
[10:21 AM, 7/5/2026] hu2: Why prohibitive?
[10:22 AM, 7/5/2026] hu5: Time, effort
[10:22 AM, 7/5/2026] hu2: How about extending the above scenario and simply pasting the same prompt to all other LLMs and see the outcomes?
[10:22 AM, 7/5/2026] hu5: Turning cases into objective questions and evaluating them with a predefined rubric is all automated.
[10:23 AM, 7/5/2026] hu5: How to judge which one is good.
[10:23 AM, 7/5/2026] hu2: I thought this required much less time and effort and also essentially depicts how LLMs are used in the real world?
[10:24 AM, 7/5/2026] hu5: Human needs to do that.
Which human should do that.
So we get 3 humans to do that for every question.
These 3 would need to be experts.
[10:24 AM, 7/5/2026] hu2: Let's just give it a try.
Currently the two LLMs have counterviews with each prioritising a separate differential as the culprit for her dryness of mouth?
[10:24 AM, 7/5/2026] hu5: The primellm was evaled manually by 3 folks and their study took jan to dec
[10:25 AM, 7/5/2026] hu5: Answer to this?
[10:27 AM, 7/5/2026] hu2: Perhaps the last human experts before everything gets automated? 😅
I guess the more important point here would be the real patient's outcome that eventually decides which was true (the biggest issue with the current LLM trials based on simulations) because as in the above example one may be able to figure out which one just by stopping the drug that appears to be priority one and then if that doesn't take care of the dryness, then stopping the next drug in order of priority
[10:29 AM, 7/5/2026] hu2: Humans who are getting directly affected, the patient's outcomes should speak for itself rather than the anecdotal opinion of an expert?
[10:30 AM, 7/5/2026] hu5: That's clinical trial then
[10:30 AM, 7/5/2026] hu2: But I agree that we'll need to choose our cases carefully and would be good if outcomes have already happened
[10:30 AM, 7/5/2026] hu2: Not really as it would be retrospective
[10:32 AM, 7/5/2026] hu5: With PaJRs we can do that post facto. So only give part info to LLM, as if patient still going through it, see what the LLM says, and compare it against what really happened.
That's what most papers do.
That said, your point about the LLM already being trained on that PaJR data is valid.
[10:32 AM, 7/5/2026] hu5: This retrospective thing is exactly what is being attempted
[10:34 AM, 7/5/2026] hu5: Challenge is only a small number of PaJRs have observation - diagnosis - action - diagnosis. Pranjal's trying to hunt for those.
[10:34 AM, 7/5/2026] hu2: So essentially nothing much changes from our prior plan of using the ODAO framework
[10:34 AM, 7/5/2026] hu5: Exactly
[10:35 AM, 7/5/2026] hu5: Below is an abstract we have been discussing. Would love comments feedback from the group. Is this the right way to do this?
Outline of this abstract could be along the following lines -
- LLMs are being increasingly used for medical purposes, ranging from applications like X to Y.
- Specialized models such as MedGemma do X, while frontier models such as GPT 5 and claude do Y
- Benchmarks such as healthbench and prime llm allow for us to benchmark different models against a common dataset.
- However, these benchmarks have been developed with X and Y, resulting in limitations P and Q
- The objective of this study is to evaluate longitudinal clinical reasoning ability of state of the art LLMs in Indian context and to introduce a multidimensional clinically m- eaningful benchmark called B for clinical grade artificial intelligence applications for Indian patients and doctors.
- The benchmark B was created using P real longitudinal cases documented with patient consent across N diseases, M geographies.
- Each of these cases comprised of N ODAO tetragrams (observation, diagnosis, action, outcome).
- These helped create a set of Q MCQs.
- A rubric was created to evaluate these across various health contexts, behavioral and demographic dimensions.
- These were evaluated by humans for X, Y, Z
- The primary outcome of the evaluation was a B score Defined as X representing N dimensions of clinical reasoning and why.
- Analysis was done including variance, T-tests, and regression models to compare AI model performance and demographic and behavioral associations.
- Results LLMs were tested across XYZ representing X responses. The scores ranged from X for model X to Y for model Y with X type of models doing better than Y type of models. Small models doing X than big models on metric e.g. quality/cost
- Failure rates ranged from X to Y.
- The cases were tested with multimodal input including blood reports, MRI scans.
- Through this study, we concluded that LLMs offer X but not Y.
- The same LLMs scored X and Y when run against benchmarks such as health bench and prime LLM, which indicates a clear difference between benchmarks of Indian cases versus US centric or global cases used in other benchmarks.
- Hence we conclude that benchmark B is a useful construct to use when testing LLMs in Indian context.
[10:36 AM, 7/5/2026] hu2: Why small number?
It should be a large number
Shall we collectively list out each one of them here or in the project group? I remember having begun that but got distracted in between.
I guess the current cases may not have the consolidated outcomes hence we can begin from the older ones?
Let's define the years
[10:37 AM, 7/5/2026] hu2: Wow 👏
[10:37 AM, 7/5/2026] hu5: Yes that would be very helpful.
Next immediate step was @hu38 validating her rubric against the one case to see if the rubric makes sense. Then basis that rubric we get other cases.
[10:38 AM, 7/5/2026] hu5: We should move this conversation to the paper group i guess
[10:38 AM, 7/5/2026] hu2: Yes I'm moving there
[10:39 AM, 7/5/2026] hu2: We should move this conversation to the paper group i guess
One challenge is I'm unable to locate that group! 😅
[10:41 AM, 7/5/2026] 42mpa: I brought this up before, yet another reason an app would be the better play 🤣🫶🏻
[10:45 AM, 7/5/2026] hu2: You are inside an app called WhatsApp!
[10:47 AM, 7/5/2026] hu2: That's a separate paper!
"Cognitive devices of human users that are beneficial to locating the exact WhatsApp group when one is a member of 1000 groups!"
[10:53 AM, 7/5/2026] 42mpa: Just gotta remember whether I took the red pill or the blue pill… and apparently I need a WhatsApp totem. 🤣
[11:24 AM, 7/5/2026] hu2: Check this out 👇
Tested with Gemini flash and PaJR search and currently PaJR search appears to win hands down? It also suggests subsequent questions that matches very well with the plan that we actually executed for this patient! Please try the prompt below with other LLMs and let me know?👇
Conversation with Gemini flash
A 50-year-old female from rural Telangana presented with a Russell's viper bite on her toe. Her bedside 20-minute whole blood clotting time (20WBCT) was positive (incoagulable blood), confirming systemic envenomation. Following standard protocol, an infusion of 5 vials of polyvalent ASV was initiated.
Approximately 45 minutes into the infusion, she developed sudden, severe dyspnea and rigors. Her condition rapidly deteriorated into anaphylactic shock, with an SpO2 of 65% and a blood pressure of 70/40 mmHg. The ASV was immediately stopped. The patient was successfully resuscitated with intramuscular adrenaline, IV fluids, corticosteroids, and oxygen.
What would be your next step for the management of her Russell's viper bite?
Answer by Gemini flash:
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.
PaJR's answer:
The management of a patient who has experienced severe anaphylaxis to antivenom (ASV) requires a careful, risk-benefit assessment. Because the patient has confirmed systemic envenomation (incoagulable blood) from a Russell's viper, the life-threatening effects of the venom must still be addressed.
Once the patient has been successfully resuscitated and is hemodynamically stable, the next steps are:
1. *Re-evaluate the need for further antivenom:* If the patient remains coagulopathic (e.g., repeat 20WBCT remains positive), the indication for antivenom persists. However, the decision to re-challenge must be made by a senior clinician, as the risk of recurrent, potentially fatal anaphylaxis is high.
2. *Preparation for re-challenge:* If antivenom is deemed essential, it should only be administered in an intensive care setting with full resuscitation equipment and staff immediately available.
3. *Controlled Re-administration:*
* *Pre-medication:* While evidence on the efficacy of prophylactic antihistamines and corticosteroids is mixed, they are often administered prior to a re-challenge in clinical practice [Adverse reactions to snake antivenom, and their prevention and treatment. — PMID:26256124](https://pubmed.ncbi.nlm.nih.gov/26256124/).
* *Slow Infusion:* If the decision is made to proceed, the antivenom should be administered at a very slow, controlled rate, often starting at a fraction of the standard infusion rate, with continuous, vigilant monitoring for any signs of recurrence.
* *Adrenaline Availability:* Adrenaline must be drawn up and ready for immediate administration at the bedside throughout the entire infusion process.
4. *Alternative Management:* If the patient's coagulopathy is not life-threatening or if the risk of re-challenge is deemed prohibitive, supportive care (e.g., fresh frozen plasma or cryoprecipitate to manage coagulopathy) may be considered, though these do not neutralize the venom itself.
The primary goal remains neutralizing the venom while minimizing the risk of a second, potentially more severe anaphylactic event. The decision to re-administer ASV is a clinical judgment based on the severity of the envenomation versus the patient's stability and the capacity for immediate rescue.
PaJR's suggested subsequent question and then answer:
What are the alternatives if antivenom cannot be safely administered?
When antivenom (ASV) cannot be safely administered due to severe anaphylaxis, the management shifts entirely to *aggressive supportive care* to sustain the patient's vital functions while the body naturally clears the venom.
In cases of Russell's viper envenomation, the primary life-threatening complications are coagulopathy (leading to bleeding) and acute kidney injury (AKI). Management focuses on the following:
* *Renal Replacement Therapy (Dialysis):* Russell's viper venom is highly nephrotoxic and frequently causes acute tubular necrosis. If the patient develops oliguria, anuria, or severe metabolic disturbances (e.g., hyperkalemia, fluid overload), *hemodialysis* is the definitive life-saving intervention [60Y Female with decreased urine output and rectal bleeding post snakebite, on haemodialysis](https://nikhilasampathkumar.blogspot.com/2023/07/introduction-this-is-online-e-log-entry.html). This supports the patient until renal function recovers.
* *Management of Coagulopathy:* If the patient has active, life-threatening bleeding, clinicians may consider the use of blood products such as *Fresh Frozen Plasma (FFP)* or *cryoprecipitate* to replace clotting factors. However, these must be used judiciously, as they do not neutralize the circulating venom and may be consumed rapidly if the coagulopathy is severe and ongoing.
* *Respiratory Support:* If the patient develops respiratory distress—whether due to pulmonary edema from renal failure or other systemic effects—mechanical ventilation may be required to maintain oxygenation and ventilation [Narketpally syn CBBLE: Participatory medical cognition Web 2.0-3.0(December 2025)](https://pajrcasereporter.blogspot.com/2025/12/narketpally-syn-cbble-participatory.html).
* *Fluid and Electrolyte Balance:* Strict monitoring of fluid intake and output is essential to prevent fluid overload in the setting of AKI. Electrolyte abnormalities, particularly hyperkalemia, must be managed aggressively according to standard critical care protocols.
In the absence of antivenom, the patient's survival depends on the ability of the intensive care team to bridge them through the period of acute organ failure. As noted in the PaJR cohort, supportive measures like dialysis are often what keep patients alive when specific antivenom therapy is limited or contraindicated [Narketpally syn CBBLE: Participatory medical cognition Web 2.0-3.0(December 2025)](https://pajrcasereporter.blogspot.com/2025/12/narketpally-syn-cbble-participatory.html).
[12:27 PM, 7/5/2026] hu35: India follows the Standard Treatment Guidelines for Snakebite Management (MoHFW, 2017, refined 2019). Key points on managing hypersensitivity while continuing treatment:
*Prevention:*
- Skin/intradermal test doses are *not recommended* — they don’t predict reactions and just delay treatment.
- Premedication is debated and not uniformly practiced. A well-known Sri Lankan RCT (widely cited in Indian practice) found low-dose subcutaneous *adrenaline* reduced acute reactions, and it’s sometimes combined with *promethazine and hydrocortisone*, though routine premedication before antivenom is only practiced by a minority of clinicians , using hydrocortisone, antihistamines, or subcutaneous epinephrine in varying combinations.
*Managing a reaction during infusion:*
1. *Stop the ASV infusion immediately.*
1. Treat the reaction — antihistamine ± hydrocortisone for milder reactions; if severe, adrenaline should be administered .
1. *Restart ASV slowly after 10–15 minutes* once the patient has recovered, keeping close observation
1. For frank anaphylaxis: IM adrenaline (0.5 mg), hydrocortisone, antihistamines, and aggressive IV fluid resuscitation .
1. *Late (serum sickness-type) reactions*, occurring days later, are treated with oral prednisolone and/or antihistamines .
*Caveat worth flagging:* Indian guidelines are notably inconsistent across hospitals in practice — many Indian hospitals don’t follow a standardized ASV protocol , and product insert instructions from manufacturers are sometimes considered unreliable. So the “official” MoHFW protocol above is the reference standard, but real-world practice varies by institution.
[2:43 PM, 7/5/2026] hu2: https://medicinedepartment.blogspot.com/2026/07/layer-3-projr-russells-viper-management.html?m=1
[2:51 PM, 7/5/2026] hu2: 👆was anyone able to read and understand this article in terms of how PaJR data could be compromised by MIA attacks? @hu5 @hu35 @hu47
[3.37 pm, 7/5/2026] hu5: Missed it earlier, saw it just now. Brilliantly done paper, German precision at its best.
What the paper shows is membership attacks. If patient x data has been used to train a model then there is way to check that. However not as relevant to PaJR trained models since PaJRs are public anyway.
[3:38 PM, 7/5/2026] hu35: To the extent that PaJR, as a public dataset, contains patients with rare, atypical, or otherwise distinctive characteristics, those individuals are at increased risk of re-identification. Recent research demonstrates that privacy risk is not evenly distributed across a dataset; instead, it is concentrated among patients who are statistical outliers or members of underrepresented groups. Uniqueness is the enemy of anonymity. The more unusual a patient is, the easier it becomes to distinguish and potentially re-identify them.
These patients occupy the long tail of the population distribution. By definition, they are less like everyone else and therefore more readily distinguishable. Whether the rarity arises from an uncommon disease, an unusual combination of clinical features, a rare demographic profile, or an exceptional sequence of events, the same principle applies: the farther a patient lies from the centre of the distribution, the greater the potential risk of re-identification.
One nuance is worth noting: the Nature paper studies membership inference attacks against AI models, whereas PaJR is a public dataset. The underlying principle—that rare or distinctive individuals face higher privacy risk—transfers well to public datasets, but the attack mechanism differs.
For PaJR, the primary concern is record linkage or the mosaic effect (combining public information from multiple sources to identify an individual), rather than membership inference against a trained model.
[3:56 PM, 7/5/2026] hu1: Please be aware that if not signed up, these chats will be taken off in 30 days (I think). Please sign up (should take seconds) and carry these chats over.
[3:58 PM, 7/5/2026] hu5: ?
[3:58 PM, 7/5/2026] hu1: On pajrhealth.com
[3:59 PM, 7/5/2026] hu5: Aah
[4:00 PM, 7/5/2026] hu1: Problem is there will be no data owner if users don't sign up - if they do, which is basically an OTP sent to their WhatsApp, we leave an audit trail
[4:02 PM, 7/5/2026] hu35: Yes—potentially, and in some cases very much so.
The article you linked
is about severe insulin resistance syndromes, many of which are exceptionally rare genetic or autoimmune disorders.
From a privacy and re-identification perspective, the issue is not simply that a patient has one of these conditions. Rather, the risk depends on how unique the patient becomes when multiple characteristics are combined.
For example, a patient with:
* a very rare insulin resistance syndrome,
* a specific age and sex,
* treatment at a particular hospital,
* a distinctive genetic variant,
* unusual physical features (e.g., lipodystrophy),
* a rare therapeutic intervention,
* and a known publication or conference presentation,
may become effectively unique within a region or even nationally. Such a patient lies in the long tail of the population distribution and therefore has a substantially higher re-identification risk than an average patient.
The Nature paper you cited earlier reaches a similar conclusion in the context of medical AI: patients with atypical or underrepresented characteristics bear disproportionately higher privacy risks because their records are more distinctive.
A useful way to state the principle is:
Privacy risk is driven not by rarity alone but by distinguishability. A rare disease is often a strong quasi-identifier, and when combined with other clinical, demographic, temporal, or geographic attributes, it may render a patient effectively unique.
So, if a PaJR record described a patient with one of these exceptionally rare syndromes and also included sufficient contextual details, that record would warrant particular scrutiny for re-identification risk. The concern is not the diagnosis by itself, but the combination of rare diagnosis and other distinguishing information.
[4:38 PM, 7/5/2026] hu5: So data owner for content in the WhatsApp chats or the searches on the site.
[4:55 PM, 7/5/2026] hu1: Searches on the site as are already transferred and linked here: https://medicinedepartment.blogspot.com/2026/07/layer-3-projr-russells-viper-management.html?m=1
[5:04 PM, 7/5/2026] hu28: Interesting experiment in class. Clever. Creative. Engaging.
The different levels of learners in the classroom surely made a difference. Near-peer mentoring is underappreciated in education.
[5:22 PM, 7/5/2026] hu1: WhatsApp is consented well, the website is still primitive with these things.
[5:24 PM, 7/5/2026] hu1: Yes will need to setup creative commons license soon.
[5:52 PM, 7/5/2026] hu28: Retrospectively prospective is what I had in mind also with PaJR , however concern for LLM training leakage.
We can do this with new cases that are protected from LLM training sets.
[5:53 PM, 7/5/2026] hu28: Another approach we could take is to use older version of LLMs with training cutoff date before the cases that we want to use.
[6:04 PM, 7/5/2026] hu28: Very good!
Few comments:
- expertise of human graders should be mentioned. 3 graders vs 2 graders with mechanism for resolving difference
- primary outcome can be a multidimensional score because models could have different strengths along different dimensions and a single score can collapse those dimensions. Can present as a radar plot.
- longitudinal real patient outcomes as grounding truth, and Indian context are two separate unique strengths. Real world ground truth is the headline.
[6:07 PM, 7/5/2026] hu5: These are great pointers thank you!
@hu38 please do keep these in mind and let's incorporate into the abstract.
[6:10 PM, 7/5/2026] hu28: Just got to this, and will move further discussion there.
