Comparison of State Board Disciplinary Actions in Board-Certified Versus Non-Board-Certified Physical Medicine and Rehabilitation Physicians
Bibliographic record
Abstract
OBJECTIVES: Our goals were to estimate how many physicians who were enrolled in a physical medicine and rehabilitation residency program are licensed to practice medicine without American Board of Physical Medicine and Rehabilitation board certification and to compare risk of state medical board disciplinary action with those who are board certified. DESIGN: We matched physicians who completed training in physical medicine and rehabilitation before 2019 with the Federation of State Medical Boards database. We compared certified versus noncertified physicians registered with Federation of State Medical Boards and frequencies of disciplinary action. RESULTS: There were 14,729 physicians with matched American Board of Physical Medicine and Rehabilitation and Federation of State Medical Boards data. Of these, 13,707 (93.1%) had attained initial American Board of Physical Medicine and Rehabilitation certification and 1022 (6.9%) had not. Certification status predicted a disciplinary action (odds ratio = 2.76; 95% confidence interval = 2.202-3.463; P < 0.001). Compared with the board-certified physicians, those who never passed part I (attempted once or more) were 4.68 times more likely to have a disciplinary action (P < 0.001), and those who passed part I with multiple attempts but failed part II (1 or more times) were 3.26 times more likely to have a disciplinary action (P = 0.013). CONCLUSIONS: Absence of American Board of Physical Medicine and Rehabilitation certification is noted in approximately 7% of physicians who undertook physical medicine and rehabilitation residency training and obtained medical licensure. These individuals are at higher risk for state medical board disciplinary action.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".