Giving Learners the Best of Both Worlds: Do Clinical Teachers Need to Guard Against Teaching Pattern Recognition to Novices?
Bibliographic record
Abstract
PURPOSE: There has been much debate in the medical education literature regarding the extent to which feature-driven and nonanalytic (similarity-based) reasoning strategies define expertise, but the relative value of teaching these strategies, together or in isolation, remains uncertain. The purpose of this study was to compare the diagnostic accuracy achieved upon receiving instruction to use each strategy in isolation to that of a combined approach. METHOD: In 2003-04, 48 undergraduate psychology students from McMaster University in Ontario, Canada, were taught to diagnose ten cardiac disorders (including normal) via electrocardiogram (ECG) presentation. Twelve students were instructed to carefully identify all features present before assigning a diagnosis (feature first). Twelve were given the same instruction with notice that some test ECGs had been seen during training (implicit combined). Twelve were simply instructed to trust familiarity and diagnose based on this impression (similarity-based). Finally, 12 students were given feature first and similarity-based instructions in combination (explicit combined). RESULTS: No difference in diagnostic accuracy was observed between the groups given the feature first (42%) and first impression (41%) instructions (p > .4), but the groups instructed to use both strategies (explicitly or implicitly) performed significantly better (56% and 53%, respectively; p < .01). CONCLUSIONS: The results support an additive model of clinical reasoning in which instructions to be feature oriented and to trust similarity improve performance in novice diagnosticians.
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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 | 0.005 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".