Correlation between craniofacial growth and upper and lower body heights in subjects with Class I occlusion
Bibliographic record
Abstract
OBJECTIVE: To correlate skeletal age, standing height, upper and lower body lengths, and selected craniofacial growth features in a sample of growing individuals, and to model craniofacial growth using multivariate regression. METHODS: This was a retrospective cross-sectional study with 447 African black boys and girls, between the ages 8 and 16 years, who attended the dental clinic at one hospital. The skeletal maturational age was determined from hand-wrist radiographs using the Greulich and Pyle atlas. Craniofacial measurements representing maxillary length (Ar-ANS), mandibular length (Ar-Gn), and lower facial height (ANS-Me) were calculated from lateral cephalograms in habitual occlusion. Body lengths were clinically measured in centimeters. RESULTS: Moderate correlations (r=0.42 to 0.68) were observed between skeletal age and the three selected craniofacial measurements. Statistically significant correlations were also found between the craniofacial measurements and both upper and lower body lengths. The mandibular length had a stronger correlation with the upper body length than with the lower body length. Multiple regression analyses to determine maxillary and mandibular lengths suggested that sex, upper and lower body lengths might be used to determine maxillary length; while skeletal age, upper and lower body lengths might help determine mandibular length. CONCLUSIONS: Based on the relatively strong correlation between upper body length and mandibular length, further research in this area may warrant its use as a predictor for mandibular growth modification timing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".