To Think Is Good: Querying an Initial Hypothesis Reduces Diagnostic Error in Medical Students
Bibliographic record
Abstract
PURPOSE: Most diagnostic errors involve faulty diagnostic reasoning. Consequently, the authors assessed the effect of querying initial hypotheses on diagnostic performance. METHOD: In 2007, the authors randomly assigned 67 first-year medical students from the University of Calgary to two groups and asked them to diagnose eight common problems. The authors presented the same primary data to both groups and asked students for their initial diagnosis. Then, after presenting secondary data that were either discordant or concordant with the primary data, they asked students for a final diagnosis. The authors noted changes in students' diagnoses and the accuracy of initial and final diagnoses for discordant and concordant cases. RESULTS: For concordant cases, students retained 84.2% of their initial diagnoses and were equally likely to move toward a correct as incorrect final diagnosis (6.9% versus 8.9%, P = .3); no difference existed in the accuracy of initial and final diagnoses: 85.9% versus 84.0% (P = .4). By contrast, for discordant cases, students retained only 23.3% of initial diagnoses, change was almost invariably from incorrect to correct (76.3% versus 0.4%, P < .001), and final diagnoses were more accurate than initial diagnoses: 80.7% versus 4.8% (P < .001). Overall, no difference existed in the accuracy of final diagnoses for concordant and discordant cases (P = .18). CONCLUSIONS: These data suggest that querying an initial diagnostic hypothesis does not harm a correct diagnosis but instead allows students to rectify an incorrect diagnosis. Whether querying initial diagnoses reduces diagnostic error in clinical practice remains unknown.
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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 | 0.030 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".