Relationship Between Performance on the NBME<sup>®</sup>Comprehensive Clinical Science Self-Assessment and USMLE<sup>®</sup>Step 2 Clinical Knowledge for USMGs and IMGs
Bibliographic record
Abstract
BACKGROUND: The Comprehensive Clinical Science Self-Assessment (CCSSA) is a web-administered multiple-choice examination that includes content that is typically covered during the core clinical clerkships in medical school. Because the content of CCSSA items resembles the content of the items on Step 2 Clinical Knowledge (CK), CCSSA is intended to be a tool for students to help assess whether they are prepared for Step 2 CK and to become familiar with its content, format, and pacing. PURPOSES: This study examined the relationship between performance on the National Board of Medical Examiners® CCSSA and performance on the United States Medical Licensing Examination® Step 2 CK for U.S./Canadian (USMGs) and international medical school students/graduates (IMGs). METHODS: The study included 9,789 participants who took CCSSA prior to their first Step 2 CK attempt. Linear and logistic regression analyses investigated the relationship between CCSSA performance and performance on Step 2 CK for both USMGs and IMGs. RESULTS: CCSSA scores explained 58% of the variation in first Step 2 CK scores for USMGs and 60% of the variation for IMGs; the relationship was somewhat different for the two groups as indicated by statistically different intercepts and slopes for the regression lines based on each group. Logistic regression results showed that examinees in both groups with low scores on CCSSA were at a higher risk of failing their first Step 2 CK attempt. CONCLUSIONS: RESULTS suggest that CCSSA can provide students with a valuable practice tool and a realistic self-assessment of their readiness to take Step 2 CK.
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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 | 0.002 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".