30. Survey Investigation of The Career Trajectories of Craniofacial-Trained Plastic Surgeons
Bibliographic record
Abstract
PURPOSE: Current literature documents a waning interest and pursuit of craniofacial surgery. This survey-based study examines CF-trained surgeons’ motives, practice patterns, and obstacles faced. METHODS: A RedCap survey consisting of 14 questions with some free-text response opportunities was sent to 310 prior CF fellow graduates within the U.S. and Canada from 2010-2021. Responses were collected until end of May, 2024. RESULTS: The response rate was 34.5% (107/310). The majority were white males ages 41-50 years. Initially 81.3% of respondents desired CF practice, and 78.5% obtained a CF job after fellowship. At the time of the survey, 71.0% endorsed having a CF practice. The distribution of job changes is demonstrated in Figure 1. Figure 2 lists negative factors associated with initial jobs identified by respondents. Most frequent cases performed among fellows were pediatric craniosynostosis (91.6%) and cleft surgery (90.7%), decreasing to 55.1% and 64.5%, respectively at the time of survey. Facial aesthetics and facial feminization surgeries increased in frequency at time of survey compared to during fellowship. CONCLUSION: Recent success in achieving a CF career is much higher than previously reported, and those with a CF practice are less likely to change careers than those in a non-CF practice after 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".