Understanding diagnosis through ACTion: evaluation of a point-of-care checklist for junior emergency medical residents
Bibliographic record
Abstract
Background Avoiding or correcting a diagnostic error first requires identification of an error and perhaps deciding to revise a diagnosis, but little is known about the factors that lead to revision. Three aspects of reflective practice, seeking Alternative explanations, exploring the Consequences of missing these alternative diagnoses, identifying Traits that may contradict the provisional diagnosis, were incorporated into a three-point diagnostic checklist (abbreviated to ACT). Methods Seventeen first and second year emergency medicine residents from the University of Toronto participated. Participants read up to eight case vignettes and completed the ACT diagnostic checklist. Provisional and final diagnoses and all responses for alternatives, consequences, and traits were individually scored as correct or incorrect. Additionally, each consequence was scored on a severity scale from 0 (not severe) to 3 (very severe). Average scores for alternatives, consequences, and traits and the severity rating for each consequence were entered into a binary logistic regression analysis with the outcome of revised or retained provisional diagnosis. Results Only 13% of diagnoses were revised. The binary logistic regression revealed that three scores derived from the ACT tool responses were associated with the decision to revise: severity rating of the consequence for missing the provisional diagnosis, the percent correct for identifying consequences, and the percent correct for identifying traits (χ2 = 23.5, df = 6, p < 0.001). The other three factors were not significant predictors. Conclusions Decisions to revise diagnoses may be cued by the detection of contradictory evidence. Education interventions may be more effective at reducing diagnostic error by targeting the ability to detect contradictory information within patient cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".