Comparison of Student Performance on Internally Prepared Clerkship Examinations and NBME Subject Examinations
Bibliographic record
Abstract
Background: This pilot study compared performance of University of Calgary students on internal clerkship examinations with corresponding National Board of Medical Examiners (NBME) subject examinations.Methods: Between April and October 2007, students completed internal and NBME subject examinations following six mandatory rotations. Local faculty within each discipline set the minimum performance level (MPL) for internal examinations. Two methods of standard setting were considered for NBME exams and a sensitivity analysis was performed. Corresponding internal and NBME examination scores were compared using McNemar’s discordant pair analysis.Results: A significant and unexpected difference in failure rate between internal and external examinations was found in all clerkships. 1.4% of students were below the MPL for internal examinations and 27.3% (modified Angoff) or 25.9% (mean Hofstee compromise) (p<0.0001 for both) for the NBME. The proportion of students below MPL for internal examinations was also below the lower limit of the Hofstee compromise (14.4%).Conclusion: Possible explanations include leniency bias in internal standard setting, discrepant content validity between local curriculum and NBME examinations, difference in student perception of examinations, and performance bias due to unfamiliar units.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".