Cosmetic Surgery Training in Plastic Surgery Residency Programs in the United States: How Have We Progressed in the Last Three Years?
Bibliographic record
Abstract
BACKGROUND: In 2006, a survey performed by Morrison et al analyzed the experience of aesthetic surgery training from the perspective of residents and their program directors in plastic surgery programs across the United States. OBJECTIVES: The authors conducted a survey to follow-up on the Morrison results three years after publication, to assess the changes in plastic surgery residency programs. METHODS: In December 2009, a 17-question survey was sent to program directors, and a 19-question survey was sent to senior residents in all Accreditation Council for Graduate Medical Education-approved plastic surgery residency programs in the United States. The questions were posed in a five-point ranking format. The two additional questions included in the senior resident survey related to career aspirations and desirable areas of additional training. Ninety-two program directors and 397 senior residents received the survey. RESULTS: Forty-four program director surveys (47.8%) and 117 (29.5%) senior resident surveys were returned. Two-thirds of programs offered a residents' clinic, which was considered the preferred method of cosmetic surgery education by residents. Residents reported increased exposure to nonsurgical procedures such as lasers and injectables. Abdominoplasty, breast augmentation, and breast reduction remained the procedures most frequently performed by residents with confidence, as in the 2006 survey. Facial aesthetic procedures, including rhinoplasty and facelift, remained challenging to residents. Many residents (55.7%) felt confident integrating cosmetic surgery into their practice. One-third of residents reported that they would apply for a cosmetic fellowship. CONCLUSIONS: This survey shows an improvement in cosmetic surgery training for plastic surgery residents in the United States, particularly in that noninvasive cosmetic treatments are being increasingly taught. Since 2006, steps have been taken to provide more comprehensive cosmetic surgery education to residents, encouraging the delivery of the safe, high-quality care expected of a board-certified plastic surgeon.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".