Reliability Evidence for Examination Cut Scores within a Medical School
Bibliographic record
Abstract
Establishing credible cut scores for performance-type examinations in health professions education can bechallenging. The authors aimed to compare the pass-fail cut-score reliability with the maximum reliabilitycut-score from multiple-choice tests (MCTs) designed on different undergraduate disciplines. Using thecross-sectional evaluation of 1370 tests from six disciplines from Porto medical school, Portugal, in 2010, thepass-fail cut-score reliability was obtained from the one-parameter logistic model of item response theory model.The test information curve achieved maximum reliability for ability levels ranging from -1.40 to -0.01 standarddeviations below the average. The pass-fail cut score for estimated ability ranged from -1.36 to 0.25. Theseresults showed that all MCTs had a pass and fail threshold of competence, and that was appropriate for themaximum information obtainable from the examination to occur at the pass and fail level; nevertheless, themaximum information was not achieved in the pass and fail level.
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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 | 0.218 | 0.434 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".