Current Patterns in the Scope of Craniofacial Fellowship Training: A Website-Based Analysis
Bibliographic record
Abstract
Background: Craniofacial surgery specializes in the treatment of the craniomaxillofacial skeleton and soft tissues. Historically, craniofacial surgery fellowship programs have most often focused on the treatment of pediatric congenital anomalies. However, the scope of the discipline has evolved, and in a recent survey of early-career craniofacial surgeons, nearly 70% of respondents reported their practice was primarily focused on adult patients. The purpose of this study is to systematically analyze the conditions and procedures advertised on craniofacial fellowship websites to ascertain the scope of training opportunities currently available within craniofacial surgery fellowship programs. Methods: In November 2024, the American Society of Craniofacial Surgeons’ (ASCFS) Fellowship Directory was used to identify endorsed craniofacial fellowships in the United States and Canada. Each program website was evaluated for the medical conditions and operative procedures or techniques to which trainees would be exposed. Results: The search identified 36 ASCFS endorsed fellowships, all of which had a website available. From the 34 websites that listed conditions and/or procedures to which trainees are exposed, 69 themes were elucidated. The top 10 themes were cleft lip and palate (91%), craniosynostosis (85%), orthognathic/jaw deformity surgery (77%), acute and secondary trauma reconstruction (71%), pediatric craniofacial surgery (68%), pediatric plastic surgery (56%), microsurgery or free flap reconstruction (56%), distraction osteogenesis (50%), vascular anomalies (47%), and craniofacial syndromes (41%). The least reported themes were neuroplastic reconstruction, tooth extraction, canthopexy, facelift, fat grafting, torticollis, transplant surgery, congenital chest, muscle reinnervation, bone substitutes, adult facial reconstruction, dermatology, preservation rhinoplasty, adult reconstruction, and surgically assisted rapid palatal expansion. Conclusions: There is a discrepancy between the training advertised on ASCFS-endorsed program websites and what recent craniofacial fellowship graduates report practicing. Increasing exposure to non-pediatric craniofacial surgery may better prepare graduates for practice in the evolving landscape of craniofacial surgery.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".