The effect of prior experience on diagnostic reasoning: exploration of availability bias
Bibliographic record
Abstract
Objectives Diagnostic reasoning has been shown to be influenced by a prior similar patient case. However, it is unclear whether this process influences diagnostic error rates or whether clinicians at all experience levels are equally susceptible. The present study measured the influence of specific prior exposure and experience level on diagnostic accuracy. Methods To create the experience of prior exposure, participants (pre-clerkship medical students, emergency medicine residents, and faculty) first verified diagnoses of clinical vignettes. The influence of prior exposures was measured using equiprobable clinical vignettes; indicating two diagnoses. Participants diagnosed equiprobable cases that were: 1) matched to exposure cases (in one of three conditions: a) similar patient features, similar clinical features; b) dissimilar patient features, similar clinical features; c) similar patient features, dissimilar clinical features), or 2) not matched to any prior case (d) no exposure). A diagnosis consistent with a matched exposure case was scored correct. Cases with no prior exposure had no matched cases, hence validated the equiprobable design. Results Diagnosis A represented 47% of responses in condition d, but there was no influence of specific similarity of patient characteristics for Diagnosis A, F(3,712)=7.28, p=0.28 or Diagnosis B, F(3,712)=4.87, p=0.19. When re-scored based on matching both equiprobable diagnoses, accuracy was high, but favored faculty (n=40) 98%, and residents (n=39) 98% over medical students (n=32) 85%, F(2,712)=35.6, p<0.0001. Accuracy for medical students was 84, 87, 94, and 73% for conditions a-d, respectively, interaction F(2,712)=3.55, p<0.002. Conclusions The differential diagnosis of pre-clerkship medical students improved with prior exposure, but this was unrelated to specific case or patient features. The accuracy of medical residents and staff was not influenced by prior exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.453 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".