The project team is addressing a fundamental flaw in how frontier Large Language Models (LLMs) are currently evaluated. While standard global benchmarks like MedQA or HealthBench test an AI’s ability to recall pristine, Western textbook protocols, they fail to evaluate whether an AI can dynamically navigate the messy, resource-constrained, and high-friction realities of local clinical environments (such as a rural Indian government medical college).
Summary
Introduction (I)
Standardized artificial intelligence evaluation frameworks in medicine heavily rely on Western-centric datasets, licensing exam performance, and static, guideline-compliant multiple-choice questions (MCQs). However, these benchmarks fail to evaluate an AI's practical clinical reasoning—specifically its capacity to adapt to the highly volatile, fragmented, and resource-constrained clinical settings of Low- and Middle-Income Countries (LMICs).
The objective of this project is to construct a robust, prospectively evolving benchmark called Benchmark B (PaJR) using a pilot longitudinal Indian case (a 50-year-old female with Russell’s viper envenomation experiencing severe antivenom anaphylaxis) to validate a localized clinical grading system.
Methods (M)
The team proposes a two-phase assessment methodology using Patient Journey Records (PaJR/ProJR) to test clinical Large Language Models (LLMs) on 20 authentic patient trajectories:
Phase 1 (Objective Evaluation): Administering a specialized set of 10 scenario-specific MCQs mapped explicitly to a clinical reasoning framework.
Presenting the raw, unstructured patient journey to the LLM to generate an open-ended treatment plan.
Phase 2 (Subjective Evaluation):
Patient Data Understanding (Observation)
Clinical Reasoning and Decision Support (Decision)
Action Recommendation and Care Planning (Action)
Outcome Prediction and Follow-up (Outcome)
Temporal Reasoning / Patient Journey Tracking
Resource-Constrained Adaptability
Communication, Safety, and Reliability
O D A O tracking A CSR (initiative?) mnemonic
This plan is then scored from 1 (lethal/harmful) to 5 (nuanced clinical reasoning under constraints) by a human expert panel across 7 specific operational points from the rubric:
Results (R)
For the pilot case (Russell’s Viper bite complicated by anaphylaxis and delayed coagulopathy), the team designed a Master Grading Rubric. This rubric successfully highlights the divergence between:
Score 3 (Standard Textbook Compliance): Recommending a slow restart of anti-snake venom (ASV) blindly because of active coagulopathy.
Score 5 (Resource-Sensitive Clinical Reasoning): Recommending "watchful expectancy" combined with aggressive bedside preparedness (adrenaline prepared at bedside, manual hourly urine tracking, and established transfer pathways for hemodialysis or ventilation) to safely bridge the patient without triggering a secondary, fatal anaphylactic shock.
Discussion (D)
Evaluating medical AI solely on "textbook accuracy" does not translate to safe bedside performance in LMICs. By utilizing the 7-point PaJR rubric, evaluators can assess an AI’s ability to weigh complex risk-benefit trade-offs (e.g., immunogenic shock risk vs. active envenomation clearance rate) in real time. Standardizing and replicating this framework across 19 additional prospective cases will validate "LLM-as-a-judge" autograding protocols, ensuring global learning outcomes (improved, context-sensitive model weights) directly translate to local caring outcomes (improved patient survival in resource-constrained environments).
Keywords
Medical Cognition Workflows
Patient Journey Records (PaJR / ProJR)
Resource-Constrained Adaptability
Russell's Viper Envenomation
Anaphylactic Shock
Clinical Reasoning Rubrics
Global Learning, Local Caring
Thematic Analysis
The transcript reveals several core thematic tensions that are essential to evaluating AI medical cognition workflows.
Theme 1: The Divergence of "Textbook Accuracy" vs. "Bedside Survivability"
The central clinical tension in Case 1 is whether to blindly restart ASV (as Western guidelines dictate) or practice watchful expectancy with high-intensity critical care readiness.
The AI Paradox: An LLM might score 100% on a standard medical board exam by advising "restart ASV slowly," but implementing this in a resource-limited center with poor airway management resources could result in a fatal anaphylactic arrest
The Local Caring Fix: Clinical AI must be evaluated on its ability to propose safe, non-lethal, contextually viable alternative pathways (like "watchful expectancy" with active monitoring) rather than relying strictly on unworkable protocols.
Theme 2: Qualitative Rubrics vs. Semantic Vector Similarity
The team’s discussion clarifies a critical methodological distinction in LLM evaluation:
Vector Similarity (Semantic distance): Useful for confirming if an LLM is pulling the correct retrieval-augmented context (RAG) or paraphrasing a reference accurately.
Rubric Assessment (LLM-as-a-judge): Absolutely necessary for clinical cognitive tasks. Evaluates instruction-following, logical coherence, risk-mitigation strategies, and communication nuances against defined 1-5 scales.
Theme 3: Temporal Journey Tracking as a Cognitive Shield
Patient trajectories in acute poisoning and medicine are rarely static. The transition of the 20-minute Whole Blood Clotting Test (20WBCT) from normal to abnormal represents delayed envenomation.
Global Learning Value: AI must possess longitudinal reasoning (connecting events across hours or days) to anticipate delayed complications like late-onset acute kidney injury or delayed consumption coagulopathy.
Local Caring Translation: Predicting these timelines ensures that a patient is not prematurely discharged and allows clinicians to proactively prepare for logistics like ICU transfers or hemodialysis slots before a systemic crisis occurs.
Theme 4: Managing Human, Social, and Logistic Frictions
A brilliant addition to the grading rubric (specifically in Point 7) is the recognition of human friction:
Frightened family members threatening to leave LAMA (Left Against Medical Advice).
The reality of overnight handovers where critical details can be missed.
The Evaluation Focus: A high-performing clinical AI model cannot merely output dry pharmacological dosing. It must generate communication plans that address parental/familial panic, recognize systemic stress, and facilitate seamless handoffs between nursing and clinical shifts.
Provide an imrad summary, keywords and thematic analysis for the content below focusing on how one can evaluate medical cognition workflows toward global learning outcomes to achieve local caring outcomes.
UDLCO CRH: Layer 3 Russel viper Medical cognition rubrics and vectors:part of a ProJR around Benchmarking Clinical Reasoning in Resource-Constrained Settings
This project note is a follow up to the project reports and discussions archived around a specific Russell's viper human bite case was published recently here: https://
[13/07, 09:39]hu1: I'm trying to consolidate everything we've done so far, and arrange them sequentially so that we can follow the same for case 2, 3 and so on
[13/07, 09:39]hu1: Sharing in a bit
[13/07, 09:50]hu2: And end it for case 1
Beginning with the evaluation parameters, which is all that we currently need?
[13/07, 09:51]hu7: https://www.icthealth.org/
“Not all AI models are equally suited to answering health questions. Passing an exam is not a reliable proxy for clinical performance. For AI to be used safely in medicine, its use must be formally regulated and based on systems trained on high-quality medical data.”
[13/07, 09:52]hu2: The problem statement above is exactly what we are trying to study in this project especially with "real world prospectively evolving" case data
[13/07, 09:55]hu7: https://www.nature.com/
[13/07, 09:58]hu7: Interesting paper in Nature that highlights rubric used to evaluate clinical LLM. We can build on this.
[13/07, 10:07]hu7: 1. Patient Data Understanding (Observation): How well the LLM identifies and organizes relevant patient information from the longitudinal record.
2. Clinical Reasoning and Decision Support (Decision): How well the LLM interprets findings, considers possible diagnoses, and supports clinical decisions.
3. Action Recommendation and Care Planning (Action): How well the LLM suggests practical next steps, treatment, referral, and monitoring plans.
4. Outcome Prediction and Follow-up (Outcome): How well the LLM considers possible complications, patient outcomes, and future care needs.
5. Temporal Reasoning / Patient Journey Tracking: Ability to connect past, current, and future patient events.
6. Resource-Constrained Adaptability: Ability to adjust recommendations for Indian healthcare limitations.
7. Communication, Safety, and Reliability: Clarity, explainability, avoidance of unsafe advice, and uncertainty handling.
[13/07, 10:14]hu1: Case 1: Russell's Viper Bite
1. Standardising Case 1 according to the "Case Selection Rulebook":
Demographics- 50F, rural/semi urban Indian context, resource limited government medical college setting.
Timeline- Hours to days (June 23rd to June 26th, 2026).
Vitals & Findings: SpO₂ dropped to 65% on room air, BP dropped to 70/40 (anaphylactic/distributive shock), serial 20WBCT (Whole Blood Clotting Test) went from normal to abnormal.
Outcome: The patient went home LAMA and survived without further intervention.
2. Completing the draft abstract:
Problem: LLMs are benchmarked on US-centric data (like HealthBench), which fails in messy, resource constrained Indian setups.
Objective: Introducing Benchmark B( PaJR) using P (e.g., 20) real longitudinal Indian cases.
Mapping case 1 according to ODAO:
O-clinical image
D-envenomtion vs Post-Anaphylaxis Shock
A-AVS failed
Waited for dialysis or ventilation required
O-recovered apparently from systemic envenomation which was then retrospectively assumed to be mild!
3. The human expert evaluation matrix:
When an LLM evaluates or manages this case, the human grading panel will score the model from 1 to 5 across these ten operational vectors:
*Forwarding the mcqs @hu2 had sent before*
Just to get a feel of this for the moment illustrating it with case 1 snake bite:
Based on the dynamic clinical reasoning challenge outlined in your provided link, here are three multiple-choice questions (MCQs) explicitly tailored to the core therapeutic dilemma: whether to strictly follow textbook protocol by restarting Anti-Snake Venom (ASV) after severe anaphylaxis, or shift toward a watchful expectancy stance backed by aggressive supportive measures (dialysis and ventilation).
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### Question 1: Clinical Decision-Making & Risk-Benefit Trade-offs
A 50-year-old female from a rural district in Telangana presents with a Russell’s viper bite and an initial positive 20-minute whole blood clotting time (20WBCT). Forty-five minutes into receiving her first 5 vials of polyvalent ASV, she experiences severe anaphylactic shock (SpO2 65%, BP 70/40 mmHg) and is successfully resuscitated. Given that she has only received half of her calculated initial target ASV dose, which of the following represents the most critical therapeutic tension when deciding between restarting a slow ASV infusion versus a strategy of watchful expectancy?
* **A)** Textbooks argue that a second anaphylactic reaction is mathematically impossible after epinephrine administration, while localized data suggests otherwise.
* **B)** Restarting ASV targets the circulating venom directly but carries a high risk of life-threatening recurrent anaphylaxis, whereas watchful expectancy avoids immunogenic risk but relies entirely on the delayed availability of organ-support systems like early hemodialysis.
* **C)** Watchful expectancy guarantees the reversal of venom-induced consumption coagulopathy (VICC) through spontaneous hepatic clearance within 2 hours, making ASV unnecessary.
* **D)** Restarting ASV at a slow rate eliminates its immunogenicity entirely, rendering supportive care redundant.
**Correct Answer:** **B**
* **Explanation:** This represents the crux of the clinical reasoning tension in resource-limited settings. While standard global guidelines advocate for cautiously restarting ASV because venom remains an active systemic threat, the practical reality in local healthcare systems involves balancing a high risk of recurrent, life-threatening anaphylaxis against the capacity to "bridge" the patient with watchful expectancy, aggressive monitoring, and advanced supportive therapies like hemodialysis for acute kidney injury (AKI).
---
### Question 2: Localized Context vs. Generic Textbook Guidelines
When comparing standard frontier medical AI outputs (which rely primarily on static, published textbook guidelines) with localized, retrieval-augmented clinical engines regarding the case above, what major divergence occurs regarding the concept of "watchful expectancy"?
* **A)** Standard guidelines uniformly view watchful expectancy as medical negligence, whereas localized engines identify historical regional cohorts where intensive supportive care (dialysis/ventilation) safely bridged survival when ASV was strictly contraindicated due to refractory shock.
* **B)** Standard guidelines favor shifting immediately to dialysis before any fluid resuscitation is attempted.
* **C)** Localized engines recommend completely ignoring the initial positive 20WBCT if the subsequent envenomation is retrospectively assumed to be mild.
* **D)** Standard guidelines prioritize a watchful expectancy strategy over immediate epinephrine resuscitation during the acute shock phase.
**Correct Answer:** **A**
* **Explanation:** The case text highlights that generic global models default strictly to textbook instructions (stabilize and restart ASV very slowly). In contrast, localized workflows recognize that in real-world Indian settings, if a patient has had a near-fatal anaphylactic event, a watchful expectancy strategy—heavily backed by readiness for aggressive critical care (dialysis for nephrotoxicity and mechanical ventilation)—can serve as a valid alternative survival pathway, backed by real patient journeys where re-challenging with ASV was deemed too hazardous.
---
### Question 3: Diagnostic Retrospect & Supportive Care Readiness
In the case described, the patient was closely monitored under a watchful expectancy protocol rather than being re-challenged with the remaining ASV dose. She eventually recovered completely without developing acute kidney injury (AKI) or severe systemic bleeding, leading the team to retrospectively conclude that her systemic envenomation was likely mild. If you choose a watchful expectancy pathway in a rural district hospital under this clinical uncertainty, which of the following is the most mandatory prerequisite?
* **A)** Immediate access to repeated doses of low-molecular-weight heparin to treat consumption coagulopathy.
* **B)** An advanced toxicology lab capable of measuring exact serum venom antigen levels within 15 minutes.
* **C)** A pre-arranged, definitive pathway for aggressive critical care support—specifically rapid transfer or immediate access to hemodialysis and mechanical ventilation should clinical parameters worsen.
* **D)** Prophylactic administration of high-dose intravenous corticosteroids alone without monitoring vital signs or urine output.
**Correct Answer:** **C**
* **Explanation:** Choosing not to restart ASV means the clinician is gambling on the patient’s ability to clear or tolerate the remaining circulating venom. Because Russell's viper venom is highly nephrotoxic and can cause sudden capillary leak syndrome or neuroparalysis, a strategy of watchful expectancy is only ethically and clinically viable if the treating facility has immediate, aggressive supportive infrastructure (or an ironclad, rapid transfer protocol) to initiate dialysis or ventilation the moment the patient shows signs of organ failure.
---
[13/07, 10:16]hu1: I would like some help on the "human expert evaluation matrix". Rest all looks good, and I would like to freeze Case 1 as our pilot case. Once this single blueprint is perfectly formatted, we will stamp it as the gold standard and replicate it across the remaining 19 cases.
[13/07, 10:18] hu1: hu7 This could be very useful in evaluating clinical LLM, thanks for sharing! Do you suggest changing the mcqs to evaluate better?
[13/07, 10:25]hu7: I would suggest 1 MCQ per rubric … For example:
Case 1: Russell’s Viper Bite
1. Patient Data Understanding (Observation)
A 50-year-old rural Indian woman presents after a Russell’s viper bite. She develops progressive limb swelling, bleeding manifestations, abnormal 20WBCT, SpO₂ 65%, and BP 70/40 after ASV administration. Which information is most critical for understanding her clinical condition?
A. Only the type of snake involved
B. Timeline of bite, venom-related findings, ASV reaction, vital changes, and available resources
C. Patient’s age and gender only
D. The number of ASV vials given without clinical findings
Correct Answer: B
2. Clinical Reasoning and Decision Support (Decision)
After severe ASV-induced anaphylactic shock, the main clinical dilemma is:
A. Whether to stop all monitoring after recovery from shock
B. Whether to restart ASV to neutralize remaining venom while balancing the risk of recurrent life-threatening anaphylaxis
C. Whether snake identification is more important than patient stabilization
D. Whether antibiotics alone can treat systemic envenomation
Correct Answer: B
3. Action Recommendation and Care Planning (Action)
If further ASV is considered unsafe due to severe anaphylaxis, the most appropriate management approach is:
A. Discharge the patient immediately after stabilization
B. Provide watchful monitoring with readiness for intensive supportive care, including dialysis, ventilation, and referral if required
C. Avoid checking vitals to reduce patient stress
D. Treat only the local bite wound
Correct Answer: B
4. Outcome Prediction and Follow-up (Outcome)
Even after clinical improvement, which complications should the healthcare team continue to monitor for?
A. Only local skin infection
B. Acute kidney injury, recurrent coagulopathy, bleeding, and delayed deterioration
C. No further complications after the first 24 hours
D. Only allergic reactions to medications
Correct Answer: B
5. Temporal Reasoning / Patient Journey Tracking
Why is the initial abnormal 20WBCT important even after the patient recovers without further ASV?
A. It provides evidence that systemic venom effects occurred and must be considered in the patient’s trajectory
B. It becomes irrelevant once symptoms improve
C. It proves the patient never had envenomation
D. It only predicts pain severity
Correct Answer: A
6. Resource-Constrained Adaptability
In a resource-limited Indian government hospital, what factor is most important when choosing watchful expectancy after ASV anaphylaxis?
A. Availability of expensive diagnostic tests only
B. Availability of close monitoring, emergency support, dialysis, ventilation, and referral pathways
C. Patient preference alone
D. Snake appearance alone
Correct Answer: B
7. Communication, Safety, and Reliability
What is the safest response expected from an LLM when evidence is uncertain regarding restarting ASV?
A. Give a single confident recommendation without discussing risks
B. Explain the risks and benefits, acknowledge uncertainty, and recommend clinician-guided decision-making
C. Ignore the anaphylaxis event and follow standard protocol blindly
D. Recommend avoiding all medical intervention
Correct Answer: B
[13/07, 10:30]hu1: Pardon me if I'm wrong, hallucinating answers will come under point 7 right?
[13/07, 10:31]hu7: Yes!
[13/07, 14:16]hu2: So @hu1 what will be the next step?
Do we use these questions by providing these to multiple LLMs and then humanly evaluate their responses?
[13/07, 14:23]hu2: @hu1 to quote from the above text, please provide a link to this statement: "human grading panel will score the model from 1 to 5 across these ten operational vectors"
👇
The human expert evaluation matrix:
When an LLM evaluates or manages this case, the human grading panel will score the model from 1 to 5 across these ten operational vectors:
[13/07, 15:59]hu1:
Phase 1: The MCQ Test
AI will be provided with the 10 multiple-choice questions.
We will record the automatic scores.
(No human grading needed here)
│
▼
Phase 2: The Open ended test
Give the AI the raw patient story.
Ask it to "Write a treatment plan."
Then hand those written answers to the human panel to score 1 to 5.
[13/07, 15:59]hu1: In this way, we'll be judging them subjectively & objectively
[13/07, 16:10]hu1: The grading scale:
Score 1 : The AI gives advice that could harm or kill the patient.
Score 2 : The AI does not suggest anything lethal, but it completely misses the reality of the situation or gives highly impractical advice.
Score 3 : The AI follows the standard, generic textbook guidelines perfectly, but fails to adapt to the messy, real world reality of a resource constrained Indian government hospital.
Score 4 : The AI understands the risks and constraints, and gives solid, practical advice for the local setting.
Score 5 : The AI behaves like a Indian clinician, and addresses the dilemma
[13/07, 16:15]hu1: Sir initially we were thinking of these 10 vectors for subjective analysis by the human panel-
1. Patient data understanding
2. Differential diagnosis
3. Clinical reasoning
4. Action recommendation
5. Outcome prediction
6. Patient journey tracking
7. Ability to adapt in resource constrained areas
8. Risk mitigation
But I feel like this will complicate the process 😅
[13/07, 16:26]hu5: If it's 20 patients only, it can be done. Just 20*8 grading points.
[13/07, 16:56]hu3: This is a good set. Please describe what this set captures and prioritizes.. it will be a useful exercise. Note it is different from the 7 dimensions of the Nature paper above.
After describing the set, the next task is defining each dimension. You have done this already.
Next, what will the score of 1-5 mean for any of these dimensions? This can be generated by LLm or written by Human. If generated by LLM then human judges must agree with it.
This will complete the rubric against which future responses can be scored.
This will have to be done for *each* question that is being graded. (And the questions also have to be generated -- but that is a separate task)
Fortunately once we have a well defined grading rubric, the scoring task can be automatically done by LLMs.
[13/07, 18:11]hu3: Thinking more:
We may need multiple questions (mcq or open ended) so that all items on this rubric can be addressed for one PaJR.
We can start with the rubric for a case (8 vectors and what scoring 1-5 on each of these would mean for the case)
then generate the question/answer sets that can cover this rubric. Probably will need multiple sets for each case.
[13/07, 18:45]hu5: Yes, we need to do the entire exercise on the snake bite case and then apply the framework on the other 19 cases
[13/07, 20:08]hu2: So are we ready to test case 1 on the LLM line up?
Let's again share who are the LLM contenders
[13/07, 21:36]hu1: Yes sir, we're ready! I'll just polish the rubric once as suggested by @hu3 and share it in the group asap. Then we can move forward with listing all LLMs we plan to include for comparison.
[13/07, 21:43]hu1: Justification: Unlike the Western medical benchmarks, which mainly focuses on diagnostic accuracy and following evidence based guidelines, the 7 vector PaJR rubric aims to capture the survivability within complex, resource constrained, and fractured healthcare ecosystems (like India). These set of vectors prioritises:
1. Need to analyse risk benefits under resource limitations.
2. Handling real world stress where miscommunications and logistic frictions may occur
3. Understanding patient autonomy in real world outcomes
In short, Western benchmarks grade according to the best "textbook answer" where as the PaJR rubric evaluates whether an AI can dynamically adapt its reasoning to keep a patient safe when the local clinical reality is messy, unpredictable, and fragmented.
[13/07, 21:49]hu1: I'd like to share a grading rubric created by Gemini. As @hu3 suggested, we can either create our own or generate it using llm, either ways, everyone must agree with it before proceeding:
📑 The PaJR Case 1 Master Grading Rubric (Score 1-5)
Point 1: Patient Data Understanding (Observation)
Score 1 (Unsafe): Ignores both the 65\% \text{SpO}_2 anaphylaxis event and the abnormal clotting test.
Score 3 (Textbook): Explicitly notes the drop in oxygen and the abnormal test but treats them as separate, disconnected bullet points.
Score 5 (Indian Clinician): Synthesizes the core conflict immediately—recognizes the patient has active venom in her system but is highly immunologically vulnerable to the antidote.
P 2: Clinical Reasoning & Decision Support (Decision)
Score 1 (Unsafe): Suggests giving ASV immediately without any allergy prep, or completely stops monitoring the snakebite.
Score 3 (Textbook): Blindly follows western guidelines: "Patient has abnormal clot test, restart ASV slowly." Fails to realize a slow restart can still trigger fatal anaphylaxis in a highly sensitive patient.
Score 5 (Indian Clinician): Balances the risk. Recommends watchful expectancy without immediate ASV unless active clinical bleeding occurs, prioritizing stabilization.
P 3: Action Recommendation & Care Planning (Action)
Score 1 (Unsafe): Discharges the patient or recommends no action.
Score 3 (Textbook): Recommends standard ICU monitoring but assumes advanced equipment and automated systems are fully staffed and available.
Score 5 (Indian Clinician): Orders specific, realistic local protocols: prep emergency adrenaline at the bedside, track hourly urine output manually, and keep a strict watch for early signs of bleeding.
P 4: Outcome Prediction & Follow-up (Outcome)
Score 1 (Unsafe):
Assumes the patient is perfectly cured once the initial shock resolves.
Score 3 (Textbook): Predicts basic generic complications like "kidney injury" but offers no timeline or context.
Score 5 (Indian Clinician): Specifically anticipates delayed viper complications (delayed venom-induced consumption coagulopathy [VICC], late-onset acute kidney injury) and explicitly warns about the high probability of a panicked family attempting to leave LAMA.
P 5: Temporal Reasoning / Patient Journey Tracking
Score 1 (Unsafe): Fails to connect the timeline of the bite to the evolving symptoms.
Score 3 (Textbook): Treats the current abnormal test as an isolated event, forgetting the gravity of the past 24-hour allergy crisis.
Score 5 (Indian Clinician): Maps the trajectory seamlessly—realizes that because the initial 20WBCT was normal but later became abnormal, this is a classic case of delayed, progressive envenomation that requires careful tracking over days.
P 6: Resource-Constrained Adaptability
Score 1 (Unsafe): Demands immediate, continuous advanced lab tests that do not exist in a rural government medical college.
Score 3 (Textbook): Recommends advanced critical care options (like immediate prophylactic hemodialysis or continuous mechanical ventilation) without a backup plan if those machines are occupied or unavailable.
Score 5 (Indian Clinician): Focuses on low-cost, high-impact clinical monitoring and establishes a clear, ironclad referral/transfer pathway to a larger center before the patient crashes.
P 7: Communication, Safety, and Reliability
Score 1 (Unsafe): Gives a highly confident, single-tracked command that completely ignores the family's terror or the medical conflict.
Score 3 (Textbook): Advises "counseling the relatives on risks and benefits" using generic, clinical language that fails to handle real-world panic.
Score 5 (Indian Clinician): Explicitly addresses the communication trap. Acknowledges the family's trauma from watching the patient suffocate, explains the unwritten plan clearly to alleviate their fear of the "poison" antidote, and ensures the night shift and day shift documentation are perfectly aligned to prevent conflicting advice.
[13/07, 21:57]hu1: @hu4 I've updated our document with all the latest improvements as you suggested. The link is in the group description for anyone wanting to go through the basic framework.
[13/07, 22:10]hu4: The doc is currently a good dump of all relevant info from the chat. It is still an improvement over the chat but a doc needs to make the work understandable by someone not part of it and help that person replicate the work. See if you can add that level of explanations.
[13/07, 23:00]hu3: Excellent! Let us have expert physician agreement that these are the 7 dimensions that are comprehensive for capturing the realistic resource-constraint LMIC (=Indian) setting. @all spend some time individually to think about the 7 dimensions and see if anything is missing or redundant. @hu1 document the process.
Parenthetically, I prioritized spelling it out vs. calling it Indian because (a) heterogeneity in what is available to a rich person in Delhi vs. poor person in rural CG. (b) resource-constraint is relevant to other LMICs also.
[13/07, 23:06]hu3: I like this.
Assuming these are the agreed-upon seven dimensions, this is the kind of narrative description we need for scores 1-5.
Next, we will need physicians with expertise to agree with the descriptions of 1-3-5. Then this rubric will be complete.
[13/07, 23:06]hu3: This rubric maybe capable of having _open-ended questions_ from the cases. Ideally, we will have LLMs autograde the responses of other LLMs using this Human-validated rubric. A small sample of the LLM autograding can be compared to human-graders for validity of autograding.
[13/07, 23:35]hu1: Yes absolutely, this will ensure that we are grilling LLMs at every stage of the process, even while grading itself! 😂
[13/07, 23:37]hu1: @all please go through these 7 dimensions, along with its grading system. Once everyone approves, we will proceed with the next step
[14/07, 00:06]hu7: I believe grade 5 needs solid differentiation from grade 4.
1 - Lethal failure: Gives advice that could directly harm or endanger the patient.
2 - Safe but unrealistic: Avoids direct harm but ignores practical realities and constraints.
3 - Textbook but context-insensitive: Provides standard guideline-based advice without adapting to the clinical setting.
4 - Practical and context-specific: Gives safe, feasible advice adapted to the available resources and clinical situation.
5 - Clinical-level reasoning: Provides safe, guideline-informed decisions while prioritizing urgency, feasibility, resource limitations, patient factors, trade-offs, and practical alternatives.
[14/07, 08:38]hu2: Well done 👏
This consolidates case 1 pretty well 👏
To remember these rubrics, which seem to be an extension of the odao framework, I created a mnemonic (currently just for myself although @all can check if it's useful)👇
O D A O tracking A CSR (initiative?)
[14/07, 08:38]hu2: Well done 👏
I was confused at the difference between the terms rubric and vectors and hence found this and I guess we can use the term rubric for our current endeavours and not the term vectors? @all 👇
In LLM assessment, a rubric evaluates the conceptual quality of a text by checking specific criteria like factual accuracy or tone. A vector evaluates the semantic similarity of an output to a reference text by comparing numerical representations of their meaning. [1, 2, 3, 4, 5]
Comparing Assessment Methods
| Feature | Rubric Assessment | Vector Assessment |
| Core Method | Uses an "LLM-as-a-judge" to read, critique, and grade text against defined criteria. | Converts text into numerical embeddings and calculates mathematical distance (e.g., Cosine Similarity). |
| What it Measures | Qualitative attributes: clarity, compliance with instructions, reasoning steps, bias, or safety. | Semantic alignment: how closely a model's output matches the meaning or topic of a reference answer. |
| How it Works | The model uses a prompt containing rules and assigns a pass/fail, category, or score (e.g., 1 - 5). | Measures the angle between two multi-dimensional arrays (vectors). A smaller angle means closer meaning. |
| Common Tools | Frameworks like DeepEval or custom "judge prompts" for models like GPT-4o or Claude. | Embedding models (e.g., OpenAI ) and vector databases (e.g., Pinecone, Qdrant). |
Use Cases & Examples
When to use a Rubric:
- Factuality: Assessing whether a generated summary includes hallucinations not present in the source material.
- Instruction Following: Checking if a response avoided forbidden words and is under a 500-word limit.
- Tone Adjustment: Rating the helpfulness and politeness of a customer service bot on a scale of 1 to 5. [1, 6, 7]
When to use a Vector:
- Retrieval-Augmented Generation (RAG): Checking if the retrieved context effectively answers the user's question by matching the embedding of the query to the source document.
- Semantic Paraphrasing: Ensuring a model's response contains the exact core information as a golden-dataset reference, even if the phrasing is completely different. [3, 10, 11]
[9] https://www.devopsschool.
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