How Do Candidates Perform When Repeating the American Board of Physical Medicine and Rehabilitation Certification Examinations?
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine the likelihood of passing the Part I and Part II American Board of Physical Medicine and Rehabilitation (ABPMR) certification examinations after initially failing. DESIGN: This was a retrospective review of candidates who had taken the ABPMR initial certification examinations between 2010 and 2014. RESULTS: Passing rates declined markedly with repeated attempts for both part I and part II. Passing rates (mean [95% confidence interval]) for part I were first attempt, 90% (87%-92%); second attempt, 58% (52%-66%); third attempt, 41% (26%-54%); fourth or greater attempt, 17% (3%-31%). For part II, the passing rates were first attempt, 87% (82%-92%); second attempt, 65% (56%-75%); third attempt, 41% (17%-65%); fourth or greater attempt, 20% (0%-59%). Those who were closer to the passing score on their initial attempt had a greater chance of passing on successive attempts. CONCLUSIONS: Passing rates for the ABPMR certification examination decline markedly with greater numbers of attempts. Those who fail again after one repeat attempt should rethink their examination preparation strategy before attempting the examination again.
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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 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".