Craniofacial Fellowship Trained Surgeons: Where Are They Now?
Bibliographic record
Abstract
INTRODUCTION: There are currently 29 craniofacial surgery fellowship training programs in the United States and Canada endorsed by the American Society of Craniofacial Surgeons and participating in the San Francisco Match. This number has increased over the last decade despite limited demand. The authors sought to evaluate the practice types and patterns of craniofacial fellowship trained surgeons. METHODS: After Institutional Review Board approval, a 20-question survey was designed to evaluate craniofacial surgeons and their practice patterns. The survey was sent to surgeons who completed accredited craniofacial fellowships in the United States or Canada from 2010 to 2018. The survey was created and distributed electronically through a private survey research center. RESULTS: There were 61 respondents (26.5% response rate), 68.8% male, and 85.2% 36–45 years old. 54.1% trained in integrated plastic surgery residency before fellowship, and 39.1% trained in general surgery followed by plastic surgery fellowship. Some had previously completed fellowships: 8 (13.1%) pediatric plastic surgery, 5 (8.2%) microsurgery, 4 (6.6%) esthetic surgery, 3 (4.9%) hand surgery, and 2 (3.3%) burn surgery. Forty-five surgeons (75%) have been in practice ≤5 years. Practice profiles were academic (49.2%), private (23.0%), and hospital employed (9.8%) with 18% in various hybrid practices. Percentage of practice dedicated to craniofacial surgery was <25% for 21 (34.4%), 25%–50% for 10 (16.4%), 51%–75% for 13 (21.3%), and >76% for 17 surgeons (27.9%) with 63.8% desiring an increase in craniofacial case volume. Surgeons’ patient populations are 14.8% pediatric only, 6.6% adult only, and 78.7% combined. They perform craniofacial trauma reconstruction (88.5%), general plastic surgery reconstruction (83.6%), cleft lip and palate repair (75.4%), craniosynostosis reconstruction (68.9%), breast surgery (54.1%), microtia reconstruction (50.8%), orthognathic surgery (50.8%), cosmetic surgery (50.8%), microsurgery (45.9%), hand surgery (36.1%), and facial reanimation (32.7%). Forty-six (75.4%) work as members of a craniofacial team. Twenty-six (42.6%) do not have any craniofacial trained partners. Twelve surgeons (19.7%) had jobs secured before beginning craniofacial fellowship, and 44 (72.1%) were able to find jobs in their desired geographic area. Forty-one (67.2%) would recommend completing a craniofacial fellowship. CONCLUSION: Craniofacial surgeons trained within the last decade are primarily in academic practice, operate on adults and children, and perform a variety of procedures. Limitations include low response rate and likelihood that surgeons who do not perform craniofacial surgery did not respond. Respondents were able to find employment in their desired location, work on a craniofacial team, and would recommend a craniofacial fellowship.
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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 | 0.004 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".