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A Proposed Rubric for Evaluating Expressed Clinical Reasoning in Large Language Model Responses

Zhangshu Joshua Jiang, Zina Ibrahim, James T. Teo

arXiv:2609.37788v1cs.CLcs.AI

TL;DR

Exam-style accuracy offers limited evidence about how models reason over patient records, and existing approaches cover different parts of clinical-reasoning assessment. This paper proposes a multidimensional rubric for scoring expressed reasoning in case-specific responses; it supplies a shared vocabulary, but its value beyond existing approaches remains to be tested.

  • Problem

    Exam-style accuracy says little about whether a model reasons well over a patient’s record, and existing approaches do not provide a shared scoring scheme for expressed reasoning over longitudinal, multi-document hospital EHR cases.

    Source: Introduction · pp. 1–2

  • Method

    The paper proposes a multidimensional rubric for judging observable information use, justification, and proposed decisions in a model response to a specified case and prompt.

    Source: Introduction · p. 2

  • Results

    The proposed rubric brings differing assessment dimensions together under a shared, clinically interpretable vocabulary, though whether it improves evaluation beyond existing approaches remains an empirical question.

    Source: Introduction · p. 2

  • Takeaways & Limitations

    The rubric supplements rather than replaces task-specific benchmarks and the case-specific criteria needed to judge clinical correctness and safety.

    Source: Introduction · p. 2, Scope and framework development · p. 3

    Case-specific reference criteria remain necessary to judge which findings, alternatives, actions, and omissions matter in an individual vignette.

    Source: Scope and framework development · p. 3

Abstract

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Rubrics support the structured evaluation of language models. We propose a rubric for assessing expressed clinical reasoning in model responses, drawing on three bodies of work: medical education assessment frameworks (ART, SCT, Key Feature Problems and OSCE); clinical LLM benchmarks (MedR-Bench, HealthBench, TIMER-Bench, DR.BENCH, PrIME-LLM and PatientSafeBench); and general LLM reasoning evaluation research, including the Factuality-Validity-Coherence-Utility taxonomy, FaithCoT-Bench and C2-Faith. We use groundedness as a clinically oriented adaptation of the taxonomy's factuality category. The rubric brings these concepts together in a multidimensional framework for scoring free-text responses to gold-standard clinical vignettes. It includes provisional behavioural anchors, applicability rules and a separate flag for case-specific safety-critical errors. General-domain frameworks inform its design but are not treated as validated clinical instruments. The rubric does not replace case-specific reference criteria or the task-specific metrics of existing benchmarks. It has not yet been tested for inter-rater reliability, construct validity or clinical utility. Its immediate purpose is to make evaluation decisions explicit and open to scrutiny before empirical testing.

Introduction

The paper motivates a rubric because exam-style accuracy does not show how LLMs reason over patient records, and existing approaches address different parts of the assessment problem. It targets expressed reasoning in responses and presents a conceptual framework with explicit scope boundaries.

Introduction

  • Exam-style accuracy says little about whether a model reasons well over a patient’s record, while existing approaches address differing parts of the assessment problem.The paper proposes a shared scoring vocabulary for expressed reasoning over longitudinal, multi-document hospital EHR cases.Source: Introduction · pp. 1–2
  • Expressed clinical reasoning is the clinically relevant quality of information use, justification, and proposed decisions observable in a response to a specified case and prompt.A rubric score judges that response under those conditions, not the model’s internal reasoning, general clinical competence, or patient benefit.Source: Introduction · p. 2
  • The crosswalk maps medical-education, clinical-benchmark, and general-evaluation precedents, but is non-exhaustive and does not treat general frameworks as validated clinical instruments.Its symbols indicate direct or partial relevance to design or interpretation, not validation of the proposed rubric or anchors.Source: Introduction · p. 2, Table 1 · p. 2

Scope and framework development

  • The framework is a conceptual synthesis, not a systematic review, formal consensus exercise, or validated instrument.Its domains describe recurring decisions a clinician-rater may face when assessing free-text responses.Source: Scope and framework development · p. 3
  • Case-specific criteria remain necessary to determine which findings, alternatives, actions, and omissions matter in an individual vignette.The framework does not itself supply those clinical reference judgements.Source: Scope and framework development · p. 3
  • Seven domains use 1–5 integer scales with behavioural anchors, and each domain can be used independently when task relevance permits.Domains 1–5 cover clinical content and justification, Domain 6 records safety-related performance and a separate case-specific error flag, and Domain 7 addresses audience-specific communication.Source: Scope and framework development · p. 3
  • General-domain frameworks serve as conceptual scaffolds; groundedness adapts factuality to combine factual correctness with support from case evidence.C2-Faith informs step-dependence and causal-coherence assessment, not whether a verbalised trace caused the answer.Source: Scope and framework development · p. 3

Domain 1 – Factual Accuracy & Groundedness

Domain 1 assesses factual accuracy and case support, while Domain 2 evaluates the quality of expressed reasoning beyond final-answer correctness. The rubric cautions that trace scores do not establish that a verbalised rationale caused the answer.

Domain 1 – Factual Accuracy & Groundedness

  • Clinical claims must be correct under current evidence-based medicine and directly supported by vignette information to meet Domain 1’s groundedness criterion.Clinically sound inference beyond stated facts is not penalised as a groundedness failure; it is credited under validity and causal reasoning.Source: Domain 1 – Factual Accuracy &… · p. 3, Table 2 · p. 3
  • Optional Domain 1 tags include unsupported-claim counts and numeric fidelity for doses, laboratory values, and vital signs.The SOAP-note Evidence Score formula is illustrative and comes from an industry write-up, not a peer-reviewed source.Source: Domain 1 – Factual Accuracy &… · p. 4

Domain 2 – Reasoning Process Quality

  • A correct final answer can still score poorly when its expressed reasoning is materially invalid, incoherent, inefficient, or incomplete.The domain evaluates reasoning quality rather than final-answer correctness alone.Source: Domain 2 – Reasoning Process Quality · p. 4
  • Reasoning-trace scores do not show that the verbalised trace caused the answer; testing that stronger claim requires perturbing relevant evidence or reasoning and observing output changes.C2-Faith informs checks of step-dependence and coverage, not causal attribution between a trace and the model’s answer.Source: Domain 2 – Reasoning Process Quality · p. 4

2a – Validity (support for each reasoning step)

The rubric evaluates whether expressed reasoning is clinically defensible, connected, and useful, with proposed behavioral anchors for validity, coherence, and efficiency.

2a – Validity (support for each reasoning step)

2b – Coherence (trace structure and flow)

2c – Efficiency (absence of redundant or irrelevant steps)

  • Efficiency measures how much expressed reasoning contributes meaningfully to clinical interpretation or decisions, not simply how brief the response is.The rubric’s percentage thresholds are provisional operational anchors, not validated MedR-Bench thresholds.Source: 2c – Efficiency (absence of redundant… · p. 5, Table 5 · p. 5

2d – Completeness (critical steps present)

Completeness concerns the case-specific reasoning needed for a defensible conclusion; diagnostic reasoning also assesses problem representation and differential generation at the relevant decision point.

2d – Completeness (critical steps present)

  • Completeness assesses whether the response includes the case-specific information and reasoning steps needed to support a defensible conclusion.Clinically valid alternative routes receive credit when they cover required decisions or provide a defensible equivalent.Source: 2d – Completeness (critical steps… · p. 5, Table 6 · p. 6

Domain 3 – Diagnostic Reasoning

  • The diagnostic-reasoning domain draws on ART, SCT, H-DDx, PrIME-LLM, and DR.BENCH frameworks.The cited frameworks correspond to data gathering and representation, diagnostic reasoning, hierarchical differentials, and sequential workflow or diagnosis-generation tasks.Source: Domain 3 – Diagnostic Reasoning · p. 6

3a – Problem Representation

  • Problem representation scores whether a one-liner accurately synthesizes key features, problem type, and relevant patient context.Illness-script framing identifies enabling conditions, fault, and clinical consequences.Source: 3a – Problem Representation · p. 6, Table 7 · p. 6

3b – Differential Diagnosis Generation

  • Differentials should be judged using evidence available at the decision point, with rank order mattering only when that evidence supports a clinical priority.A defensible differential should not be penalized solely because the eventual diagnosis was not initially ranked first.Source: 3b – Differential Diagnosis Generation · p. 6, Table 8 · p. 6

3c – Diagnostic Test Selection

The rubric scores test selection for clinical appropriateness, causal claims for defensibility, and temporal reasoning within its applicable scope.

3c – Diagnostic Test Selection

3d – Causal Reasoning Appropriateness

  • Causal reasoning is scored for whether claims are clinically defensible and appropriate to the question, not for reaching a higher level of causal complexity.The causal sub-dimension is marked N/A when no causal interpretation is called for and the response makes no causal claim; unsupported causal claims remain assessable.Source: 3d – Causal Reasoning Appropriateness · p. 7, Table 10 · p. 7

Domain 4 – Temporal Reasoning

  • Temporal reasoning applies when longitudinal evidence or disease trajectory is relevant; trajectory interpretation additionally asks whether the model connects change appropriately to clinical decisions.The composite temporal score is the mean of applicable sub-dimension scores, each on a 1–5 scale.Source: Domain 4 – Temporal Reasoning · pp. 7–8, Table 11 · p. 8

Domain 5 – Uncertainty Handling, Bayesian Updating, & Metacognition

The rubric covers uncertainty calibration, evidence-sensitive belief updating, and context-seeking, alongside a safety-first approach to red flags and escalation triggers.

Domain 5 – Uncertainty Handling, Bayesian Updating, & Metacognition

  • The uncertainty domain draws on clinical benchmarks and medical-education approaches to uncertainty, while overconfidence is identified as a measurable medical-reasoning failure mode.The cited precedents include HealthBench, Griot et al., and the Script Concordance Test.Source: Domain 5 – Uncertainty Handling,… · p. 8

Overall uncertainty calibration

Evidence-sensitive belief updating

  • Belief updating assesses whether competing diagnoses change appropriately as new evidence arrives, without requiring explicit numerical probabilities.Score this only when sequential information makes belief change observable; use NA for a static snapshot.Source: Evidence-sensitive belief updating · p. 8, Table 13 · p. 9

Context-seeking behaviour

  • Context-seeking assesses whether the model identifies missing contextual information and requests what is most likely to reduce relevant uncertainty.Table 14 supplies proposed anchors at scores 1, 3, and 5.Source: Context-seeking behaviour · p. 9, Table 14 · p. 9

Domain 6 – Clinical Safety & Red Flag Recognition

  • A missed red flag or safety-critical escalation trigger should reduce the overall score even when the rest of the response is high quality.The cited SOAP-note benchmark’s 50% safety weighting is illustrative of the design principle, not a peer-reviewed standard.Source: Domain 6 – Clinical Safety & Red Flag… · p. 9

6a – Escalation/Emergency Recognition

The excerpt distinguishes flexible escalation credit from mandatory harm-avoidance checks, then covers sycophancy and communication through additional rubric domains.

6a – Escalation/Emergency Recognition

  • Escalation recognition uses a flexible list, crediting coverage of enough acceptable actions or pathways rather than requiring every item.More than one appropriate pathway may exist.Source: 6a – Escalation/Emergency Recognition · p. 9

6b – Harm Avoidance (contraindications, dangerous omissions)

  • Harm avoidance uses a strict list: every contraindication and essential caveat is mandatory, and any omission caps the score.Source: 6b – Harm Avoidance… · p. 10

6c – Sycophancy Resistance

Domain 7 – Communication Quality & Tailoring

7a – Clarity and Structure

  • Table 18 provides behavioural scoring anchors on a 1–5 scale for clarity and structure.Source: Table 18 · p. 10

7b – Audience Calibration

The excerpt provides scoring guidance for audience and instruction domains, applicability, aggregation, safety flags, and task-specific weighting.

7b – Audience Calibration

  • Table 19 provides behavioural scoring anchors on a 1–5 scale for audience calibration.Source: Table 19 · p. 11

7c – Instruction Following

  • Table 20 provides behavioural scoring anchors on a 1–5 scale for instruction following.Source: Table 20 · p. 11

Scoring and Interpretation

  • Score applicable sub-dimensions on anchored 1–5 scales, mark irrelevant domains not applicable, and exclude them from the composite.Domain 4 is not scored for a static single-encounter vignette without a meaningful temporal component.Source: Scoring and Interpretation · p. 11

Case-specific safety-critical error flag

  • Flag prespecified case-specific contraindicated actions, essential safety omissions, or clinically material delays; report the flag separately because a high composite cannot erase it.An absent flag means only that no prespecified rubric-defined safety-critical error was identified, not that the response is safe for clinical use.Source: Case-specific safety-critical error flag · p. 11

Task-specific weighting

  • The proposed weighting profiles are design options, not validated weights; prespecify and justify a task-relevant profile and report unweighted domain results.Weights for inapplicable domains are removed and remaining weights renormalised to sum to one.Source: Task-specific weighting · p. 12

Discussion

The discussion motivates a multidimensional rubric using prior assessment and benchmark findings, while identifying limitations that require validation and constrain use.

Discussion

  • The rubric draws on medical education’s structured reasoning assessment, benchmark findings on reasoning weaknesses and poor text-similarity alignment, and a shift toward hierarchical evaluation.The authors argue a multidimensional rubric better aligns with evaluation practice moving beyond flat aggregate metrics.Source: Discussion · p. 12
  • The rubric scores expressed reasoning, not whether that explanation faithfully reflects the process that produced the answer; process faithfulness needs separate intervention testing [5] [6].One proposed approach changes or removes evidence and observes whether the model’s conclusion changes.Source: Discussion · p. 12
  • Even trained examiners achieved only fair-to-good inter-rater reliability on ART reasoning scores, so formal experiments need clinician training and anchor-based calibration.Source: Discussion · p. 12
  • Because clinical LLM benchmarks may fail construct-validity tests against real-world data, the authors propose piloting the rubric on 10–20 expert-annotated vignettes.Source: Discussion · p. 12
  • Domain 4 applies only to multi-visit or longitudinal vignettes; the proposed 1,000–3,000-example TIMER-Bench subset is a project constraint, not a sample-size recommendation [13].Source: Discussion · p. 12
  • Sycophancy scoring requires multi-turn vignettes with deliberate adversarial pushback, so it cannot be scored on single-turn cases.The authors recommend using it selectively in alignment experiments.Source: Discussion · p. 12
  • LLM-w-Ref reports Pearson correlations of 0.68 to 0.87 with expert ratings and may automate scoring of Domains 1–3, subject to validation against human annotations.Source: Discussion · p. 12
  • General-domain frameworks and benchmarks inform specific design choices but are not evidence that those domains are clinically validated [25] [42] [43].Source: Discussion · p. 12

Appendix

The appendix provides a scoring sheet, instructions for marking inapplicable domains, and task-type weighting options.

Complete Scoring Sheet

Scoring Sheet Template

Weighting Options

Relationship to Key Benchmarks

Table 23 maps medical-education frameworks, clinical benchmarks, and general reasoning research to rubric domains as conceptual or methodological precedents, not as validation of the rubric. The cited sources contribute different assessment ideas, while retaining their original scope and limitations.

Relationship to Key Benchmarks

  • Table 23 treats each mapping as conceptual or methodological relevance, not validation; it labels general-domain sources and non-peer-reviewed resources explicitly.Alaa et al. [53] motivate interpreting benchmark scores relative to a specified clinical construct and intended use.Source: Table 23 · p. 18, Relationship to Key Benchmarks · p. 20
  • ART informs validity, completeness, differential diagnosis, and metacognition; the Script Concordance Test contributes structured judgment under uncertainty.ART was developed and validated for human learners, so its constructs are adapted rather than transferred as a validated LLM instrument; the Script Concordance Test is not a general free-text reasoning rubric.Source: Relationship to Key Benchmarks · p. 19
  • Key Feature Problems focus assessment on critical case decisions, while OSCE contributes structured observable-performance and communication assessment.Both are medical-education design precedents, not validated scoring thresholds or direct instruments for free-text LLM reasoning.Source: Relationship to Key Benchmarks · p. 19
  • MedThink-Bench [16] informs intermediate reasoning-point assessment, H-DDx hierarchical credit for related diagnoses, and Pearl’s Ladder [47] task-appropriate causal reasoning.The rubric’s differential rank anchors are proposed rather than H-DDx-validated thresholds; excellent causal reasoning need not exhibit all three levels when a lower level suffices.Source: Relationship to Key Benchmarks · p. 19
  • Medical metacognition evaluation motivates separating expressed confidence from observed performance; PatientSafeBench informs emergency recognition and harmful-advice avoidance.PatientSafeBench is distinct from Microsoft’s PatientSafetyBench.Source: Relationship to Key Benchmarks · pp. 19–20
  • Patient-facing single-turn examples inform misinformation, overconfidence, and harm avoidance, while EchoBench and Chen et al. [49] inform resistance to misleading input.Disagreement alone should not earn credit without clinical justification; these sources do not operationalize multi-turn sycophancy or all forms of emergency escalation.Source: Relationship to Key Benchmarks · p. 20
  • The Factuality–Validity–Coherence–Utility taxonomy [25] informs groundedness, validity, and coherence; PRMBench [43] analogizes detecting missing prerequisites and unsupported steps.Utility is treated as a broader task-level property rather than a one-to-one mapping to a rubric sub-dimension.Source: Relationship to Key Benchmarks · p. 20
  • FaithCoT-Bench and C2-Faith [41, 42] inform reasoning-trace checks, but do not establish clinical validity or that verbalized reasoning caused the answer.C2-Faith assesses controlled step-dependence, causal-coherence violations, and coverage deletions; the Omi Health protocol is not peer-reviewed or clinically validated.Source: Relationship to Key Benchmarks · p. 20
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