Validated Assessment Tools and Maintenance of Certification in Plastic Surgery: Current Status, Challenges, and Future Possibilities
Bibliographic record
Abstract
BACKGROUND: The transition to the Next Accreditation System is well underway, and a shift toward competency-based assessment in the form of milestones is now the standard. A significant effort has been completed by the Plastic Surgery Milestones Working Group to develop specific milestones and assessment tools for plastic surgery training. METHODS: The history of the development toward competency-based assessment was reviewed. Data regarding the trends and regulations associated with board certification and the role of maintenance of certification were reviewed. RESULTS: The work of the Plastic Surgery Milestones Working Group has sparked interest in assessment and created an opportunity for further development. The efforts toward validating assessment tools by our colleagues working in other surgical specialties serve as a suitable roadmap for further progress. Board certification is an integral part of successful practice and should be regarded as an expectation. Despite the burdens associated with maintenance of certification, it serves a valuable function in ensuring optimal patient care and is often retrospectively seen as an important component of practice. CONCLUSIONS: The competency-based milestones are the new standard, and work on this new methodology of assessing plastic surgery trainees is expected to continue. Accurate assessment is critical to the pathways for board certification and maintenance of certification, which serve important roles for all parties involved in the delivery of medical care.
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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 | 0.211 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".