Current status and trends in subspecialty certification in physical medicine and rehabilitation
Bibliographic record
Abstract
BACKGROUND: There is a need to better understand the overall state of sub-specialization in physical medicine and rehabilitation (PM&R). OBJECTIVE: To examine the status and trends in subspecialty certification for each of the seven subspecialties approved for American Board of Physical Medicine and Rehabilitation (ABPMR) diplomates. DESIGN/SETTING: Retrospective analysis of deidentified information from the ABPMR database. PARTICIPANTS: Physicians certified by ABPMR through 2019. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: For each subspecialty, we examined: (1) the number of certificates issued to ABPMR diplomates; (2) the recertification rate; (3) the yearly trends for total active, new, and expired certificates; and (4) for ABPMR-administered subspecialties, recertification rates for those entering the subspecialty through fellowship completion versus a "grandfathered" practice pathway. RESULTS: Of 11,421 ABPMR diplomates in the United States in 2019, a total of 3560 (31.2%) had 3985 active subspecialty certificates. Pain Medicine (PM) was the most common subspecialty certification (15.5% of all ABPMR diplomates) followed by Sports Medicine (SM, 6.6%), Brain Injury Medicine (BIM, 4.8%), Spinal Cord Injury Medicine (SCIM, 4.2%), Pediatric Rehabilitation Medicine (PRM, 2.5%), Neuromuscular Medicine (NMM, 0.7%), and Hospice and Palliative Medicine (HPM, 0.5%). For diplomates with more than one subspecialty certification, PM and SM was the most frequent combination. Both the recertification rate and the end of practice track eligibility influenced certification trends differently for individual subspecialties. The average number of new certificates added annually for every subspecialty was higher before than after the temporary practice track-based eligibility ended; the difference was statistically significant (p < .05) for SCIM, PM, SM, and NMM. The recertification rate for all subspecialties combined was 73.4%. For the subspecialties (SCIM, PRM) for which these data were available, fellowship candidates had higher recertification rates than those grandfathered through a practice track. CONCLUSION: This report informs stakeholders about the state and evolution of subspecialty certification in PM&R over time.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".