Computed Tomography–Based Morphometric Analysis of Extended Strip Craniectomy for Sagittal Synostosis
Bibliographic record
Abstract
BACKGROUND: The purpose of this study is 2-fold: (1) to identify computed tomography (CT)-based morphometric parameters that differentiate the normal skull from one with sagittal synostosis and (2) to evaluate correction of sagittal synostosis with extended strip craniectomy and postoperative helmeting based on morphometric parameters. METHODS: An institutional review board-approved, retrospective review was carried out at the Hospital for Sick Children for all patients who underwent an extended strip craniectomy and postoperative helmeting for sagittal synostosis from 1999 to 2005. Inclusion criteria consisted of patients who underwent a routine craniofacial CT preoperatively and 12 months postoperatively. Craniofacial CT scans of age-matched control subjects were used for preoperative and postoperative comparison. RESULTS: Thirty-nine patients with sagittal synostosis met inclusion criteria. Median age at preoperative CT was 3.0 months. Nine control subjects were identified, with a median age at CT scan of 5.0 months. Patients with sagittal synostosis preoperatively had a significantly longer maximum cranial length, smaller maximum cranial breadth, more acute frontal takeoff and occipital incline angles, lower cephalic index, and an anteriorly positioned vertex. Postoperative CT scans (median, 17.0 months) were compared with 10 control subjects (median, 19.0 months). Patients with sagittal synostosis postoperatively had equivalent maximum cranial breadth, frontal takeoff, and occipital incline angles as compared with controls. Sagittal synostosis patients remained with a significantly longer maximum cranial length, lower cephalic index, and anteriorly positioned vertex. CONCLUSIONS: Twelve months following extended strip craniectomy and helmeting for sagittal synostosis, CT-based morphometric analysis demonstrated correction of cranial breadth, frontal bossing, and occipital bulleting. Skull length and vertex position did not fully correct.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".