Predictors of Performance on the American Board of Physical Medicine and Rehabilitation Maintenance of Certification Examination
Bibliographic record
Abstract
BACKGROUND: Maintenance of certification (MOC) in Physical Medicine and Rehabilitation is a process of lifelong learning that begins after successfully completing an Accreditation Council for Graduate Medical Education (ACGME)-accredited residency and passing the American Board of Physical Medicine and Rehabilitation (ABPMR) Part I and Part II Examinations. We seek to identify factors predictive of successful MOC Examination performance. OBJECTIVE: To identify characteristics predictive of successful completion on the ABPMR MOC Examination. DESIGN: Retrospective review. SETTING: American Board of Physical Medicine and Rehabilitation database review. PARTICIPANTS: 4,545 diplomates who completed the MOC Examination between January 2006 and December 2017. METHODS: MOC Examination performance was the primary outcome variable. Performance on Part I and Part II Examinations were independent variables. Additional potential predictors evaluated included year of MOC cycle in which examination was taken, years of practice since residency completion, age, and subspecialty certification. MAIN OUTCOME MEASURES: Performance on MOC Examination. RESULTS: Age at time of MOC Examination was inversely correlated with examination score (r = -0.14, P < .001). Similarly, as time since completion of residency training increased, MOC scores declined. Passing the Part I Examination on first attempt predicted a 98% MOC pass rate, compared to 90% for those who failed initially. MOC performance was highly correlated with Part I performance (r = 0.59, P < .001) and Part II performance (r = 0.32, P < .001). Although MOC performance was similar for those taking the examination in years 7 - 10 of their cycle (97% pass rate), those taking the examination after more than 10 years of the cycle had a significantly lower performance (85% pass rate, P < .01). CONCLUSIONS: Better performance on the MOC Examination is associated with better performance on Part I and Part II Examinations, taking the examination earlier in the 10 year cycle, younger age, and less time since completion of training. Diplomates who are at higher risk for failing the examination may need to prepare differently for MOC Exam than those who are more likely to pass. LEVEL OF EVIDENCE: III.
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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 | 0.011 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".