Relationship Between Performance on the NBME Comprehensive Basic Sciences Self-Assessment and USMLE Step 1 for U.S. and Canadian Medical School Students
Bibliographic record
Abstract
BACKGROUND: This study examined the relationship between performance on the National Board of Medical Examiners Comprehensive Basic Science Self-Assessment (CBSSA) and performance on United States Medical Licensing Examination Step 1. METHOD: The study included 12,224 U.S. and Canadian medical school students who took CBSSA prior to their first Step 1 attempt. Linear and logistic regression analyses investigated the relationship between CBSSA performance and performance on Step 1, and how that relationship was related to interval between exams. RESULTS: CBSSA scores explained 67% of the variation in first Step 1 scores as the sole predictor variable and 69% of the variation when time between CBSSA attempt and first Step 1 attempt was also included as a predictor. Logistic regression results showed that examinees with low scores on CBSSA were at higher risk of failing their first Step 1 attempt. CONCLUSIONS: Results suggest that CBSSA can provide students with a realistic self-assessment of their readiness to take Step 1.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".