Can American Board of Physical Medicine and Rehabilitation Part 2 Board Examination Scores be Predicted from Rotation Evaluations or Mock Oral Examinations?
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine whether either Physical Medicine and Rehabilitation residency performance on core competency evaluations or on practice mock oral examinations is correlated to performance on future American Board of Physical Medicine and Rehabilitation Part 2 board-certifying examination. DESIGN: This is a retrospective cohort study of residents who took part 2 of the American Board of Physical Medicine and Rehabilitation certification examination between 1995 and 2011 (N = 31 or 38 or 67). RESULTS: The postgraduate year 4 mock oral examination average achieved significance in correlation analysis (Spearman ρ, 0.0391; P = 0.030). Patient care and a composite average of the other core competencies evaluations were also significantly correlated with performance on part 2 of the board-certifying examination (Spearman ρ, 0.329; P = 0.044). The only independent variable that was uniquely predictive was postgraduate year 4 mock oral examinations (χ = 7.09; P = 0.029). More specifically, when controlling for rotation performances, residents with higher mock oral examination scores were 9.6 times (Exp B = 9.6; 95% confidence interval, 1.2-80; P = 0.036) more likely than those one grade lower to achieve the upper half on oral board examinations vs. either of the lower 2 quartiles. CONCLUSIONS: The postgraduate year 4 mock oral examinations and the core competency evaluations composite are each predictive of performance on American Board of Physical Medicine and Rehabilitation part 2 examination. Further research into this area, with a larger sample size and with multiple institutions, would be helpful to allow for a better measurement of these evaluation tools' effectiveness.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".