Hindsight Bias in Clinical Temporal Reasoning: How Future Data Exposure Affects Large Language Model Judgment

1. Introduction

• Later evidence is not inherently biasing; the target failure is changing a judgment about what was supportable at an earlier reference point.The question and antecedent evidence must remain fixed while access to post-reference information varies.Source: §1 · p. 1
• Prior clinical vignette studies found hindsight bias, but the cited evaluations did not test it across a corpus of real longitudinal clinical cases.The benchmark also differs from ExAnte’s general-domain temporal-leakage objective by measuring outcome-consistent shifts in clinical judgments [Liu et al., 2026].Source: §1 · p. 2
• The benchmark pairs masked and complete trajectories with prospective reference answers and predefined hindsight traps to measure outcome-conditioned judgment shifts.It evaluates the same question under both information conditions.Source: §1 · p. 2, Figure 1 · p. 2
• Across two cohorts and multiple input representations, temporal masking reduced hindsight bias without reducing accuracy.The benchmark’s complementary metrics include ∆HTR, AIR, and HBR.Source: §1 · p. 2

2. Related Work

• General temporal benchmarks test event ordering, temporal arithmetic, duration, and multistep reasoning, while ExAnte tests post-cutoff leakage in non-clinical tasks.The present work instead evaluates whether later patient events shift an earlier clinical judgment toward a hindsight trap.Source: §2 · p. 2
• Longitudinal-record benchmarks assess instruction following and question answering, while newer temporal tasks address timestamp grounding, evidence accumulation, or windowed ICU decisions.This benchmark pairs the same question and pre-cutoff evidence with masked and complete timelines to measure whether answers change directionally.Source: §2 · pp. 2–3
• Clinical case-report benchmarks evaluate diagnostic reasoning or examination, diagnosis, and treatment planning; this work asks whether a conclusion was supportable before later events.It builds on prior work reconstructing time-localized sepsis trajectories from case reports.Source: §2 · p. 3

3.1. Study design and data source

• 171 PMC Open Access case reports comprise 40 sepsis cases and 131 GLP-1/diabetes cases, spanning contrasting acute and longitudinal clinical trajectories.The cases are represented by original narratives and independently created human-annotated and LLM-generated timelines.Source: §3.1 · p. 3, §3.2 · p. 3

3.2. Clinical representations

• Each textual time series orders clinical events by relative time, preserving event order when it diverges from the retrospective narrative.Questions were generated from the original narrative paired with the human timeline; ten per case yielded 1,710 questions.Source: §3.2 · p. 3, §3.3 · p. 3

3.3. Question construction

• Questions cover accuracy, hindsight-sensitive judgments, or both, with a per-case 2/3/5 category split and formats including free response, true/false, and multiple choice.Visible questions omit explicit temporal references, so prospective reasoning depends on supplied context rather than an overt instruction.Source: §3.3 · pp. 3–4

3.4. Temporal masking and cutoff position

• Masked timelines retain events at or before each question’s cutoff, whereas full timelines retain the complete trajectory; the same numeric cutoff applies to both TTS sources.Cutoff position is normalized by chronological event index, with 0 and 1 denoting the first and last recorded events.Source: §3.4 · p. 4
• Synthetic narratives are generated from the corresponding masked or full timelines, aligning narrative and TTS information scope; seven input conditions vary narrative and timeline sources.TTS-only conditions isolate temporal masking, while some original-narrative conditions can still reveal post-cutoff information.Source: §3.4 · p. 4, §3.6 · p. 4

3.5. Answer generation

• Four models answered each question with only the visible question fields and assigned context, without retrieval, tools, or web access.A sepsis sensitivity analysis added instructions to disregard final outcomes for GPT and GLM.Source: §3.5 · p. 4

3.7.1. Individual metrics

• Accuracy measures agreement with the prospective reference, while hindsight trap rate measures agreement with the outcome-consistent trap; answer instability records any answer change, regardless of direction.Free-response accuracy scores are 0, 0.5, or 1, with 0.5 denoting partial correctness.Source: §3.7.1 · p. 5, §3.7.2 · p. 5

3.7.3. Statistical analyses

• Confidence intervals use a case-clustered bootstrap with 2,000 replicates, and four clinical annotators reviewed an error-enriched 10% case sample for question reasonableness and answer agreement.Sepsis and GLP-1/diabetes were analyzed separately; effects relative to zero were significant when the 95% interval excluded zero.Source: §3.7.3 · p. 5, §3.8 · p. 5

Full-information baseline

• 14–15% of responses were hindsight-biased when GPT answered narrative questions in the full-information baseline condition.HTR was significantly greater than zero in both datasets.Source: §4 · p. 5

Temporal isolation

• In Conditions 1.1 and 1.2, ∆HTR was small and non-significant despite significant AIR and HBR, because the original narrative remained available.These comparisons isolate masking the TTS, not removal of future information from the entire input.Source: §4 · p. 6

TTS source

• Human-TTS variants generally had slightly higher absolute accuracy than LLM-TTS variants, but reconstructed event content or timing could omit information or place it across a cutoff.The human TTS was the reference representation used during question generation.Source: §4 · pp. 6–7

Paired masking results

• 16.0% AIR and 5.5% HBR in Sepsis Condition 2.2 exemplify paired shifts; all three metrics were significantly positive under strict masking across both datasets.AIR measures answer instability, while HBR identifies shifts from initially correct prospective answers toward predefined hindsight traps.Source: §4 · pp. 5–6, Figure 4 · p. 7

Synthetic narrative

• Synthetic narrative Conditions 3.x tracked strict-masking Conditions 2.x, with all hindsight metrics significantly positive and magnitudes closer to 2.x than attenuated 1.x.The authors suggest the weaker 1.x effects reflect leakage from the unmasked original narrative rather than narrative prompting itself.Source: §4 · p. 7

Accuracy

• No paired condition showed a significant accuracy difference, so masking reduced hindsight bias without a detectable accuracy penalty.Point estimates for accuracy differences varied in direction, with no reliable evidence that full-case access improved accuracy over masked input.Source: Figure 3 · p. 6, §4 · p. 7

Sensitivity analysis

• Explicit hindsight warnings generally reduced hindsight metrics, particularly under strict masking, but AIR and HBR remained positive across paired conditions.Accuracy remained broadly similar in this Sepsis sensitivity analysis for GPT and GLM.Source: §4 · p. 7

Human validation

• Human review judged 95.9% of validation questions clinically reasonable, with 98.7% agreement on prospective answers and 97.1% on hindsight traps.Inter-annotator agreement was 91.8% for question reasonableness, 99.2% for prospective answers, and 94.1% for hindsight traps.Source: §4 · p. 7

5. Discussion

• Under strict temporal masking, ∆HTR ranged from 5.8% to 9.6% and HBR from 3.3% to 5.5%, while ∆Acc was statistically insignificant.AIR and HBR were positive across paired input settings; the unpaired 14–15% hindsight trap rate is descriptive, not a causal estimate.Source: §5 · p. 8
• Human review supported the validity of GPT-generated questions, prospective answers, and hindsight traps, including in a validation set enriched for challenging cases.The authors argue that this validation strengthens confidence that invalid questions or labels did not drive the central findings.Source: §5 · p. 8
• Convergence across AIR, accuracy, HTR, and HBR—measures that rely on different annotation components—makes a single-label or single-metric artifact less likely.AIR requires only an answer change; accuracy uses the prospective reference, HTR the hindsight trap, and HBR combines both.Source: §5 · p. 8
• Limitations include ambiguous reference times, LLM-judge scoring, unusual published cases, and potentially imprecise timestamps that may not reflect clinical-state onset or duration.Explicit time anchors could reduce task ambiguity, but may not resemble deployments where models must infer temporal context.Source: §5 · p. 8
• Future work should extend evaluation to routine longitudinal EHRs, larger expert-reviewed samples, and counterfactual outcome controls, while studying training and architecture strategies.The authors also recommend restricting inputs to decision-time information and reporting directional outcome-sensitive measures alongside accuracy.Source: §5 · p. 8

6. Conclusion

• The benchmark tests whether later clinical information changes LLM judgments about an earlier decision point.It is a paired benchmark, comparing masked and complete trajectories.Source: §6 · p. 8
• Across two clinical cohorts and four models, complete trajectories increased movement toward hindsight traps, whereas temporal masking reduced the shifts without detectable accuracy loss.The conclusion states that retrospective evaluation can overstate prospective reasoning performance.Source: §6 · p. 9
• The findings motivate evaluation protocols that restrict model inputs to information available at the target decision time.This recommendation is framed for evaluations of earlier clinical decisions.Source: §6 · p. 9

Appendix A. Detailed Literature Review

• General temporal benchmarks assess event order, temporal arithmetic, duration, multistep reasoning, or inference under explicit cutoffs, but do not test clinical hindsight-trap shifts.The review contrasts general-domain temporal tasks with measuring whether later events in the same patient trajectory move an earlier clinical judgment toward a trap.Source: Appendix A · p. 12
• Longitudinal clinical QA benchmarks assess retrieval, temporal synthesis, instruction following, evolving diagnosis, or prospective performance against retrospectively informed labels.This benchmark instead fixes the question and prospective reference target while comparing masked and complete-record exposure.Source: Appendix A · p. 12
• Case-report reasoning resources evaluate final conclusions or their explanations; this benchmark measures when a conclusion was supportable and how post-cutoff events change the same answer.The paired comparison uses the same question and pre-cutoff evidence, then exposes either a masked or complete timeline.Source: Appendix A · pp. 12–13

Appendix B. Experimental conditions

• Condition 0 exposes the complete retrospective narrative, condition 1.x keeps that narrative while truncating TTS, and condition 2.x uses only TTS for a precisely truncated comparison.Condition 0 represents an upper bound of potential hindsight exposure; condition 1.x provides only partial control because its complete narrative may imply outcomes.Source: Appendix B · p. 13
• Condition 3.x pairs a synthetic narrative derived from masked TTS with the same masked TTS, retaining narrative context while strictly limiting both sources to pre-cutoff information.Unlike condition 1.x, the synthetic narrative cannot implicitly reveal later outcomes; unlike condition 2.x, the condition retains narrative context.Source: Appendix B · p. 13

Appendix C. Prompts

• Question generation specifies 10 questions per case across free-response, multiple-choice, and true/false formats, targeting accuracy (20%), hindsight bias (30%), or both (50%).Questions should be difficult, clinically focused, and avoid explicit time references so temporal access is controlled by masking rather than question wording.Source: Appendix C · p. 14
• Prompts prioritize ambiguous early presentations and make the hindsight trap more attractive than the conservative prospective answer, using TTS to target out-of-order events and tight event clusters.Ordinal choices use exact probability ranges, with the correct answer and trap at opposite ends of the scale.Source: Appendix C · p. 14
• Answer prompts vary visible inputs by condition: narrative only, narrative plus TTS, or TTS only; when sources conflict, models use TTS for ordering and timing and narrative for clinical detail.Models are instructed to answer from visible case information, use general medical knowledge only to interpret clinical language, and provide case-grounded evidence.Source: Appendix C · pp. 15–16
• The condition 3 synthetic-case prompt converts timeline entries into a narrative while requiring explicit timeline support and prohibiting invented patient details or speculative additions.When the timeline is ambiguous or incomplete, the prompt directs conservative reporting.Source: Appendix C · pp. 16–17

• Accuracy and HTR scores use 1, 0.5, or 0 for fully, partially, or not correct or trap-reflecting answers, respectively.Source: Appendix C · p. 17
• HBR credit requires a correct masked answer and a full answer reflecting the trap; null traps set HTR and HBR to 0, and clinical equivalence defines full-equals-masked.Source: Appendix C · p. 17

Appendix D. Distribution of Masking Cutoffs

• Relative cutoff position is the cutoff event’s chronological index normalized to [0, 1], where 0 and 1 mark the beginning and end of the timeline.It measures event-sequence position rather than elapsed clinical time.Source: Appendix D · p. 18
• Mean and median cutoff positions were near each dataset’s timeline midpoint, while broad ranges show questions span much of the clinical trajectory.Relative position indexes the cutoff event within the recorded sequence, not elapsed clinical time.Source: Appendix D · p. 18, Table D.1 · p. 18
• Masking points span nearly the full relative-position range, with Sepsis cutoffs tending earlier than GLP cutoffs, particularly mid-timeline.Figure D.1 shows the distribution across the clinical timeline.Source: Appendix D · p. 18

Appendix E. Results for Other Models

• Accuracy remained generally high with modest full–masked differences, while masked HTR was consistently lower across models in strict 2.x and 3.x conditions.Full and masked HTR were similar in the 1.x conditions.Source: Appendix E · p. 18, Figure E.7 · p. 23, Figure E.8 · p. 23
• Under strict masking, ∆HTR was consistently positive and AIR increased substantially; positive HBR indicates some answers shifted toward the hindsight trap when post-cutoff information returned.The paired-metric pattern appeared across all four model families, although effect sizes varied across models and datasets.Source: Appendix E · p. 18, Figure E.9 · p. 24
• Accuracy and hindsight susceptibility appear to capture distinct aspects of model behavior, and the association between lower model performance and masking degradation was not monotonic.The authors therefore caution against interpreting cross-model differences straightforwardly.Source: Appendix E · p. 22

Appendix F. Sensitivity Analysis with Explicit Hindsight Warning

• Explicit warnings attenuated hindsight-biased responding in GPT and GLM, with the clearest reductions in Conditions 2.1–3.2, but AIR and HBR remained positive across paired conditions.Figures F.1–F.4 report accuracy, HTR, and paired hindsight metrics for the two models.Source: Appendix F · p. 25, Figure F.1 · p. 25, Figure F.2 · p. 25
• Accuracy remained broadly similar between prompt versions, so reduced hindsight-related metrics were not attributed to general deterioration in task performance.This pattern was observed for both GPT and GLM.Source: Appendix F · p. 25
• The authors conclude that models respond to explicit anti-hindsight instructions, but prompt-level mitigation alone is insufficient to eliminate the effect.AIR and HBR remained positive despite the warning.Source: Appendix F · p. 25

Appendix G. Human Validation

• Four clinical annotators—one physician and three medical or physician assistant trainees—independently reviewed two non-overlapping 5% case subsets.They assessed question reasonableness and agreement with prospective-reference and hindsight-trap answers.Source: Appendix G · p. 27
• The validation sample was targeted rather than uniformly drawn: cases were sampled with weights to stress-test annotations relevant to model errors and hindsight effects.A 0.3-unit increase in EPS corresponds to an approximately 2.72-fold increase in relative sampling weight.Source: Appendix G · p. 27
• Across 170 questions reviewed by two annotators each, sample agreement ranged from 95.9% to 98.7%, and inter-annotator agreement ranged from 91.8% to 99.2%.The review covered question reasonableness, prospective answers, and hindsight-trap answers.Source: Appendix G · p. 27
• Inverse-probability-weighted estimates closely matched sample estimates, but uncertainty intervals would require case-level resampling.The estimates account for the cohort-specific error-enriched sampling design.Source: Appendix G · p. 27, Table G.1 · p. 28