Interrater Reliability in the American Board of Physical Medicine and Rehabilitation Part II Certification Examination
Bibliographic record
Abstract
OBJECTIVE: The design of medical board certification examinations continues to evolve with advances in testing innovations and psychometric analysis. The potential for subjectivity is inherent in the design of oral board examinations, making improvements in reliability and validity especially important. The purpose of this quality improvement study was to analyze the impact of using two examiners on the overall reliability of the oral certification examination in physical medicine and rehabilitation. DESIGN: This was a retrospective quality improvement study of 422 candidates for the American Board of Physical Medicine and Rehabilitation Part II Examination in 2020. Candidates were examined by examiner pairs, each of whom submitted independent scores. Training for all 116 examiners included examination case review, scoring guidelines, and bias mitigation. Examiner performance was analyzed for both internal consistency (intrarater reliability) and agreement with their paired examiner (interrater reliability). RESULTS: The reliability of the Part II Examination was high, ranging from 0.93 to 0.94 over three administrations. The analysis also demonstrated high interrater agreement and examiner internal consistency. CONCLUSIONS: A high degree of interrater agreement was found using a new, two-examiner format. Comprehensive examiner training is likely the most significant factor for this finding. The two-examiner format improved the overall reliability and validity of the Part II Examination.
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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 | 0.107 | 0.202 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".