Comparability of the National Board of Medical Examiners Comprehensive Clinical Science Examination and a Set of Five Clinical Science Subject Examinations
Bibliographic record
Abstract
PURPOSE: Accreditation standards require medical schools to use comparable assessment methods to ensure students in rotation-based clerkships and longitudinal integrated clerkships (LICs) achieve the same learning objectives. The National Board of Medical Examiners (NBME) Clinical Science Subject Examinations (subject exams) are commonly used, but an integrated examination like the NBME Comprehensive Clinical Science Examination (CCSE) may be better suited for LICs. This study examined the comparability of the CCSE and five commonly required subject exams. METHOD: In 2009-2010, third-year medical students in rotation-based clerkships at the University of British Columbia Faculty of Medicine completed subject exams in medicine, obstetrics-gynecology, pediatrics, psychiatry, and surgery for summative purposes following each rotation and a year-end CCSE for formative purposes. Data for 205 students were analyzed to determine the relationship between scores on the CCSE (and its five discipline subscales) and the five subject exams and the impact of clerkship rotation order. RESULTS: The correlation between the CCSE score and the average score on the five subject exams was high (0.80-0.93). Four subject exam scores were significant predictors of the CCSE score, and scores on the subject exams explained 65%-87% of CCSE score variance. Scores on each subject exam-but not rotation order-were statistically significant in predicting corresponding CCSE discipline subscale scores. CONCLUSIONS: The results provide evidence that these five subject exams and the CCSE measure similar constructs. This suggests that assessment of clerkship-year students' knowledge using the CCSE is comparable to assessment using this set of subject exams.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".