Teaching posttraining: Influencing diagnostic strategy with instructions at test.
Bibliographic record
Abstract
It is believed that medical diagnosis involves two complementary processes, analytic and similarity-based. There is considerable debate as to which of these processes defines diagnostic expertise and how best to teach clinical diagnosis and reduce diagnostic errors. The purpose of these studies is to document the use of these strategies in medical students. We shifted the balance in use of these processes and improved diagnostic accuracy with instructions given posttraining at the moment of diagnosis. Analytic processing reflecting the degree to which cases contain the diagnostic rules was indexed by the rate of accuracy on typical versus atypical cases (typicality effect). Similarity-based processing reflecting the degree to which cases resemble previously encountered cases was indexed by the rate of accuracy on similar versus dissimilar cases (similarity effect). Two studies are presented illustrating that diagnosis involves the coordination of analytic and similarity-based processes and that differential instruction given at test shifts the balance in the use of these processes. Study 1 illustrated that participants adopting an analytic strategy exhibit a larger effect of typicality. Participants adopting a similarity-based strategy exhibit a larger effect of similarity. The diagnostic approach of students given no instructions was predominantly analytic. Dual instructions in which participants first employed similarity-based processing followed by the application of rules improved overall accuracy. Study 2 investigated two versions of dual instructions and illustrated that assessing a case with the rules of diagnosis first may inhibit the subsequent use of similarity-based reasoning. The implications for diagnostic expertise and pedagogy are discussed.
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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 | 0.002 | 0.058 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".