Introduction. Ongoing challenges in pediatric craniofacial surgery
Bibliographic record
Abstract
S urgical procedures for craniofacial disorders are among the most common operations in pediatric neurosurgery.Typically, pediatric neurosurgeons collaborate with plastic surgery colleagues to manage these challenging conditions.This is a broad discipline with many unanswered questions.This issue of Neurosurgical Focus attempts to fill some of those gaps, beginning with a snapshot of practice patterns in the United States.Sullivan et al. find that practice patterns include open cranial vault surgery and endoscopic methods but also an increasing use of cranial distraction procedures.The common questions from parents about sports participation after craniosynostosis surgery are addressed in a survey of surgeons in a collaborative network.Most neurosurgeons allow unrestricted participation unless a cranioplasty was performed.The complexity of caring for patients with challenging craniofacial cleft and hypertelorism is discussed in a paper by As'adi et al.In that study, the authors point out the risk of infection and CSF leakage.Several articles tackle the ongoing discussion of open versus endoscopic surgical techniques.Less invasive single-suture synostosis procedures have reduced the need for transfusion and resulted in shorter hospital stays without obvious differences in other complications or outcomes.Two papers describe useful methods of evaluating cranial shape in children with sagittal synostosis (vs unaffected controls).In one study, the authors used photogrammetry, and in the other, optical surface scanning was
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".