P.197 To stave or not to stave? The impact of barrel-stave osteotomy on cephalometric measurements in endoscopic repair of sagittal craniosynostosis
Bibliographic record
Abstract
Background: Treatment for sagittal craniosynostosis often involves endoscopic suturectomy (ES) followed by helmet therapy, with cranial shape improvement assessed via cephalic index (CI). The effect of adding barrel-stave osteotomy (BSO) to ES on CI outcomes remains controversial. This study evaluated the impact of BSO on operative burden and postoperative cranial deformity in patients undergoing surgical correction of sagittal craniosynostosis. Methods: A retrospective review of 85 patients treated for sagittal craniosynostosis at BC Children’s Hospital (2010–2021) compared patients undergoing ES alone (n=18) and ES+BSO (n=67). Demographics, operative burden (anesthesia and surgical time, blood loss, hospital stay), and longitudinal CI measurements were analyzed. Mixed effects modeling controlled for age, preoperative CI, and helmet duration. Results: Operative burden did not differ significantly between treatment groups (p > 0.05). The median follow-up duration for CI measurements was 56.0 months. While preoperative CI was similar (67.4 vs. 66.8, p=0.61), CI was significantly improved in the ES+BSO group at all postoperative intervals (p ≤ 0.02). Mixed effects modeling confirmed that BSO independently improved CI (effect size 2.21, p=0.001). Conclusions: In our series, the addition of BSO to ES significantly improved immediate and long-term cranial deformity without increasing operative burden, supporting its use in sagittal craniosynostosis correction.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".