Three-dimensional morphometric analysis of cranial sutures – A novel approach to quantitative analysis
Bibliographic record
Abstract
Differences in complexity of cranial suture forms on the endocranial (i.e., deep) and ectocranial (i.e., superficial) skull surfaces have been noted in the literature, indicating through thickness three-dimensional (3D) suture variability depending on the chosen section and necessity for considering the complete 3D structure in many cases. This study aims to evaluate the variability of suture morphology through the skull thickness using a rat model, and to provide more robust metrics and methodologies to analyze suture morphology. X-ray micro-computed tomographic (μCT) imaging methods were utilized in order to provide internal structure information. Methods were developed to isolate and analyze sutures widths and linear interdigitation index (LII) values on each adjacent offset transverse plane of the μCT datasets. LII was defined as the curved path length of the suture divided by the linear length between the ends of the region of interest. Scans were obtained on 15 female rats at ages of 16, 20, and 24 weeks (n = 5/age). Samples were imaged at 18 μm resolutions with 90 kV source voltage, 278 μA source amperage, and 0.7° increments. Suture widths and LII values were compared using a Kruskal-Wallis test. 3D variability in local suture widths within individuals, as well as through thickness variabilities in planar widths and LII was observed. Kruskal-Wallis tests for bulk through thickness averaged suture widths and LII were found to be statistically insignificant, despite clear geometric differences through suture thicknesses. Although the bulk morphometric variability between age groups was found to be statistically insignificant, the 3D variability within individuals point to the importance of analyzing suture form using 3D metrics when studying suture development, response to functional activity, or morphometry in general.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".