Quantitative and Qualitative Assessment of Morphology in Sagittal Synostosis
Bibliographic record
Abstract
Consensus remains lacking regarding the optimal surgical treatment modality for sagittal synostosis. There is, however, wide agreement that objective analytical methods are required to demonstrate the characteristic morphology of the condition and to substantiate the benefits of specified surgical techniques. Simple calculated anthropomorphic indices, such as the cranial index, are commonly used but fail to provide satisfactory representation of morphology, which is far more complex than can be represented by its simple length-width ratio. Techniques to provide more comprehensive, yet practical, assessment of morphology are needed for analytic purposes. Herein, we introduce vector analysis as an objective, computed tomography (CT)-based morphometric technique for assessment of cranial morphology; this work represents the first application of the technique mid-sagittal vector analysis (MSVA). MSVA is a single plane application that was devised to address dysmorphology in sagittal synostosis. It was our hypothesis that MSVA would quantitatively and qualitatively depict preoperative morphology and postoperative correction in specific regions. Sixteen patients undergoing cranial reshaping surgery for sagittal synostosis were included in the study. All patients underwent routine preoperative and 1 year postoperative CT scans, from which the MSVA was derived. MSVA is a radial vector analysis in which distances to the cranial surface are measured from a single reference point origin in the sagittal plane. Preoperative morphology, characterized by respective vectors, was analyzed in three regions: the frontal, vertex, and occipital regions. Comparison with postoperative paired data was conducted for each patient. The analysis of postoperative change demonstrated (1) decrease in prominence in the frontal and occipital regions, (2) increase in height and forward translation of the vertex, and (3) ability to distinguish and qualify frontal versus occipital bossing and correction thereof. We conclude that the longitudinal differences associated with scaphocephaly are well characterized and differentiated by MSVA. Quantitative and qualitative assessment identifies three relevant regions affected by the condition and its treatment: the frontal, vertex, and occipital regions. The transverse dimension is not addressed in this single plane analysis; a more comprehensive application will require additional planes of analysis and the development of a normative database.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".