Geometric morphometric analysis of craniofacial growth between the ages of 12 and 14 in normal humans
Bibliographic record
Abstract
AIM: There is great variation of growth among individuals. The question whether patients with different skeletal discrepancies grow differently is biologically interesting but also important in designing clinical trials. The aim of the present study was to evaluate whether growth direction depends on the initial craniofacial pattern. SUBJECTS AND METHOD: The sample consisted of 350 lateral cephalograms of 175 subjects (91 females and 84 males) followed during normal growth without any orthodontic treatment. The examined ages were 12 (T1) and 14 (T2) years. The cephalograms were obtained from the American Association of Orthodontists Foundation (AAOF) Craniofacial Growth Legacy Collection (Burlington, Fels, Iowa, and Oregon growth studies). We digitally traced 15 curves on each cephalogram, comprehensively covering the craniofacial skeleton, and located 127 points on the curves, 117 of which were sliding semilandmarks and 10 fixed. Procrustes alignment, principal component analysis and two-block partial least squares analysis were performed, after sliding the semilandmarks to minimize bending energy. RESULTS: The first 10 principal components (PCs) described approximately 71 per cent of the total shape variance. PC1 was related to shape variance in the vertical direction (low/high angle skeletal pattern) and PC2 was mainly related to shape variance in the anteroposterior direction (Class II/Class III pattern). PC3 was mainly related to the shape variance of the mandibular angle. All subjects shared a similar growth trajectory in shape space. We did not find any correlation between the initial shape and the magnitude of shape change between T1 and T2, but males showed a greater shape change than females. The direction of shape change was moderately correlated to the initial shape (RV coefficient: 0.14, P < 0.001). CONCLUSIONS: The initial shape of the craniofacial complex covaried weakly with the direction of shape change during growth.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".