Differential Weighting of Errors on a Test of Clinical Reasoning Skills
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PROBLEM STATEMENT AND BACKGROUND: Examinees can make three types of errors on the short-menu questions in the Clinical Reasoning Skills component of the Medical Council of Canada's Qualifying Examination Part I: (1) failing to select any correct responses, (2) selecting too many responses, or (3) selecting a response that is inappropriate or harmful to the patient. This study compared the information provided by equal and differential weighting of these errors. METHOD: The item response theory nominal model was applied to fit examinees' response patterns on the 1998 test. RESULTS: Differential error weighting resulted in improved model fit and increased test information for examinees in the lower half of the achievement continuum. CONCLUSION: Differential error weighting appears promising. The pass score is near the lower end of the achievement continuum; therefore, this approach may improve the accuracy of pass-fail decisions.
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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 | 0.019 | 0.709 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it