The Relationship Between the American Board of Anesthesiology Part 1 Certification Examination and the United States Medical Licensing Examination
Bibliographic record
Abstract
BACKGROUND: The graduate medical education community uses results from the United States Medical Licensing Examination (USMLE) to inform decisions about individuals' readiness for postgraduate training. OBJECTIVE: We sought to determine the relationship between performance on the USMLE and the American Board of Anesthesiology (ABA) Part 1 Certification Examination using a national sample of examinees, and we considered the relationship in the context of undergraduate medical education location and examination content. METHODS: Approximately 7800 individuals met inclusion criteria. The relationships between USMLE scores and ABA Part 1 pass rates were examined, and predictions for the strength of the relationship between USMLE content areas and ABA performance were compared with observed relationships. RESULTS: Pearson correlations between ABA Part 1 scores and USMLE Steps 1, 2 (clinical knowledge), and 3 scores for first-taker US/Canadian graduates were .59, .56, and .53, respectively. A clear relationship was demonstrated between USMLE scores and pass rates on ABA Part 1, and content experts were able to successfully predict the USMLE content categories that would least or most likely relate to ABA Part 1 scores. CONCLUSIONS: The analysis provided evidence on a national scale that results from the USMLE and the ABA Part 1 were correlated and that success on the latter examination was associated with level of USMLE performance. Both testing programs have been successful in conceptualizing many of the knowledge areas of interest and in developing test content to reflect those areas.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".