Does the Physical Medicine and Rehabilitation Self‐Assessment Examination for Residents Predict the Chances of Passing the Part 1 Board Certification Examination?
Bibliographic record
Abstract
BACKGROUND: Each year, residents in accredited United States Physical Medicine and Rehabilitation (PMR) residency programs can take the American Academy of Physical Medicine and Rehabilitation (AAPM&R) Self-Assessment Examination for Residents (SAE-R). This 150-question, multiple-choice examination is intended for self-assessment of physiatric knowledge, but its predictive value for performance on the part 1 American Board of Physical Medicine and Rehabilitation Certification Examination (ABPMR-CE) is unknown. OBJECTIVE: To investigate the predictive value of the SAE-R in relation to the part 1 ABPMR-CE. DESIGN: Retrospective study. METHODS: Data were analyzed from first time takers of the part 1 ABPMR-CE during a 5-year period from 2010 through 2014 who took at least 1 SAE-R in the third or fourth postgraduate year (PGY) of residency. MAIN OUTCOME MEASUREMENTS: Raw scores from the SAE-R were compared with scaled scores on the part 1 examination. Regression models analyzed the predictive value of the SAE-R total score for each PGY level. RESULTS: SAE-R raw scores increased an average of 5.5 points between the PGY 3 and PGY 4 year. PGY3 SAE-R raw scores accounted for 24.8% and PGY4 SAE-R scores for 27.1% of the variance in part 1 ABPMR-CE scores (P < .0001). Residents who obtained a raw score greater than 80 (53% correct) on the SAE-R had an 80% or greater chance of passing the ABPMR-CE. Scores greater than 90 (60% correct) on the SAE-R were associated with a 95% chance of passing the ABPMR-CE. CONCLUSION: The SAE-R scores provide some information regarding the likelihood of passing the part 1 certification examination. This study supports the SAE-R as a means of providing PMR residents with feedback regarding their level of knowledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".