Long-term 3D CT follow-up after endoscopic sagittal craniosynostosis repair
Bibliographic record
Abstract
OBJECTIVE: Surgical treatment of sagittal synostosis involves various surgical modalities. Long-term follow-up issues include increased intracranial pressure, secondary sutural fusion, incomplete reossification, and suboptimal cosmetic appearance. The authors' objective in this study was to review their long-term endoscopic surgical results in children with sagittal synostosis using 3D CT. METHODS: The authors reviewed the long-term results of their first 38 patients who underwent endoscopic sagittal synostosis repair at age 16 weeks or younger. A standard vertex craniectomy with biparietal wedges was done in each case. After surgery, the children were fitted with a helmet, which they wore until 8 months of age. Patients were followed up for 5 years or longer, at which point a 3D CT scan was obtained. The authors examined data on the cranial index, area of bony defect, presence or absence of secondary sutural fusion, neosuture formation, and scalloping of the inner table of the skull. RESULTS: Thirty-two of 38 children met inclusion criteria. There was a small but significant recession of the cranial index after the completion of helmeting (from 0.772 after completion of helmeting to 0.755 at 5 years). Of 32 children, 14 had a bony defect area > 4 cm2. Three children had secondary sutural fusion (two unilateral coronal, one bicoronal). Ten of 32 patients had partial neosuture formation. CONCLUSIONS: The authors report their experience with 32 of their first 38 children who underwent endoscopic sagittal synostosis repair at 16 weeks of age or younger. With a minimum duration of 5 years, this is the longest clinicoradiological follow-up utilizing 3D CT to date in children with sagittal synostosis treated with endoscopic surgery. The authors report detailed measurements of bony loss, adjacent sutural fusion, and neosuture formation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".