Do the National Board of Medical Examiners (NBME) subject examinations predict success on the Medical Council of Canada qualifying examination (MCCQE) Part 1
Bibliographic record
Abstract
NBME subject examinations are used by many Canadian medical schools to evaluate students during their clerkship rotations. Although studies show strong correlations between NBME scores and the American licensing examination, published research is not available comparing these examinations to scores on the MCCQE Part I. The purpose of this study is to determine if the NBME subject examination scores for pediatrics, internal medicine, surgery, psychiatry, and obstetrics/gynecology can identify students at risk of poor performance on the MCCQE Part I. Student NBME scores and MCCQE Part I scores from 4 academic years were analyzed. The MCCQE Part I scores included the total score as well as the multiple choice (MCQ), clinical decision making (CDM), and subject-specific MCQ scores. A total of 224 sets of student scores were analyzed. Moderate correlations were found between the NBME and the total MCCQE Part I score (r= 0.519-0.613, p=0.000) as well as the MCQ score (r=0.506-0.605, p=0.000). There were low-moderate correlations between each of the NBME scores and their respective subject-specific MCQ (r=0.333-0.489, p=0.000) and CDM scores (r=0.297-0.360, p=0.000). Six students failed the MCCQE Part I and 3 of these students failed at least 2 of the 5 NBME examinations. The mean scores on each of the NBME examinations were significantly lower for students who failed the MCCQE Part I than for those who passed (p<0.004). The NBME subject examinations show moderate correlations when compared to the total MCCQE Part I score and may help to predict which students are at risk of failing the Canadian licensing examination.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".