Comparing Cephalic Index and Midsagittal Vector Analysis in Assessing Morphology in Sagittal Synostosis: A CT-Based Morphometric Analysis
Bibliographic record
Abstract
Introduction: Assessment of cranial dysmorphism in sagittal synostosis is often subjective but objective measures can be applied. These include cephalic index (CI) and midsagittal vector analysis (MSVA). Objective: To assess discriminant validity, construct validity, and responsiveness of CI and MSVA measured from computed tomography (CT) in patients with sagittal synostosis. Methods: Patients with nonsyndromic isolated sagittal synostosis with complete preoperative (n = 30) and postoperative (n = 13) CT data were included. Age-matched control group (n = 24) comprised of normocephalic patients who underwent CT for reasons related to trauma. Outcome Measures: Retrospective CT evaluation of CI and MSVA was conducted and correlated with a dysmorphism numeric rating scale (D-NRS) that measured surgeon-rated severity of sagittal synostosis. Responsiveness of CI and MSVA was evaluated using dysmorphism global rating of change (D-GRC). Results: Thirty patients with sagittal synostosis were demographically similar to 24 normocephalic patients. The difference in CI and MSVA was statistically significant between normocephalic and scaphocephalic patients. Cephalic index had a good correlation with D-NRS ( r = −0.665, ρ = −0.667), but not with MSVA ( r = 0.250, ρ = 0.203). Change in CI ( r = 0.738, ρ = 0.657) was well correlated with D-GRC, but not with MSVA ( r = −0.409, ρ = −0.301). Conclusion: Cephalic index appears to quantify the severity of sagittal synostosis better than MSVA. Cephalic index also has better responsiveness than MSVA to measure a reduction in severity of disease; however, MSVA is a better descriptive craniometric measurement. Midsagittal vector analysis was able to quantify the shift in morphology in sagittal synostosis following surgical treatment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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".