Maintenance of Certification, Maintenance of Public Trust
Bibliographic record
Abstract
The Maintenance of Certification program has been well received by many physicians but has faced significant opposition from others who complain that it is overly tedious, costly, and irrelevant to their practice. This article offers a consolidated and concise history of the program and a summary of what plastic surgeons need to know to successfully complete the American Board of Plastic Surgery's own Maintenance of Certification requirements. The authors have justified each step of the board's Maintenance of Certification process in terms of how it improves the quality of care delivered to plastic surgery patients. Finally, a summary of research is presented that demonstrates both that the public supports the maintenance of certification process for all physicians and that continuing education and formal assessment and improvement initiatives have been linked in multiple studies to a better and more evidence-based medical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".