The Effectiveness of Cognitive Forcing Strategies to Decrease Diagnostic Error: An Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Cognitive forcing strategies, a form of metacognition, have been advocated as a strategy to prevent diagnostic error. Increasingly, curricula are being implemented in medical training to address this error. Yet there is no experimental evidence that these curricula are effective. DESCRIPTION: This was an exploratory, prospective study using consecutive enrollment of 56 senior medical students during their emergency medicine rotation. Students received interactive, standardized cognitive forcing strategy training. EVALUATION: Using a cross-over design to assess transfer between similar (to instructional cases) and novel diagnostic cases, students were evaluated on 6 test cases. Forty-seven students were immediately tested and 9 were tested 2 weeks later. Data were analyzed using descriptive statistics and a McNemar chi-square test. CONCLUSIONS: This is the first study to explore the impact of cognitive forcing strategy training on diagnostic error. Our preliminary findings suggest that application and retention is poor. Further large studies are required to determine if transfer across diagnostic formats occurs.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".