Value of General Medical Knowledge Examinations in Performance Assessment of Practicing Physicians With Potential Competence and Performance Deficiencies
Bibliographic record
Abstract
INTRODUCTION: Problems with a physician's performance may arise at any point during their career. As such, there is a need for effective, valid tools and processes to accurately assess and identify deficiencies in competence or performance. Although scores on multiple-choice questions have been shown to be predictive of some aspects of physician performance in practicing physicians, their relationship to overall clinical competence is somewhat uncertain particularly after the first 10 years of practice. As such, the purpose of this study was to examine how a general medical knowledge multiple-choice question examination is associated with a comprehensive assessment of competence and performance in experienced practicing physicians with potential competence and performance deficiencies. METHODS: The study included 233 physicians, of varying specialties, assessed by the University of California, San Diego Physician Assessment and Clinical Education Program (PACE), between 2008 and 2012, who completed the Post-Licensure Assessment System Mechanisms of Disease (MoD) examination. Logistic regression determined if the examination score significantly predicted passing assessment outcome after correcting for gender, international medical graduate status, certification status, and age. RESULTS: Most physicians (89.7%) received an overall passing assessment outcome on the PACE assessment. The mean MoD score was 66.9% correct, with a median of 68.0%. Logistic regression (P = .038) was significant in indicating that physicians with higher MoD examination scores had an increased likelihood of achieving a passing assessment outcome (odds ratio = 1.057). DISCUSSION: Physician MoD scores are significant predictors of overall physician competence and performance as evaluated by PACE assessment.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".