Teaching from the clinical reasoning literature: combined reasoning strategies help novice diagnosticians overcome misleading information
Bibliographic record
Abstract
OBJECTIVE: Previous research has revealed a pedagogical benefit of instructing novice diagnosticians to utilise a combined approach to clinical reasoning (familiarity-driven pattern recognition combined with a careful consideration of the presenting features) when diagnosing electrocardiograms (ECGs). This paper reports 2 studies demonstrating that the combined instructions are especially valuable in helping students overcome biasing influences. METHODS: Undergraduate psychology students were trained to diagnose 10 cardiac conditions via ECG presentation. Half of all participants were instructed to reason in a combined manner and half were given no explicit instruction regarding the diagnostic task. In Study 1 (n = 60), half of each group was biased towards an incorrect diagnosis through presentation of counter-indicative features. In Study 2 (n = 48), a third of the test ECGs were presented with a correct diagnostic suggestion, a third with an incorrect suggestion, and a third without a suggestion. RESULTS: Overall, the instruction to utilise a combined reasoning approach resulted in greater diagnostic accuracy relative to leaving students to their own intuitions regarding how best to approach new cases. The effect was particularly pronounced when cases were made challenging by biasing participants towards an incorrect diagnosis, either through mention of a specific feature or by making an inaccurate diagnostic suggestion. DISCUSSION: These studies advance a growing body of evidence suggesting that various diagnostic strategies identified in the literature on clinical reasoning are not mutually exclusive and that trainees can benefit from explicit guidance regarding the value of both analytic and non-analytic reasoning tendencies.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".