Heritability of mandibular cephalometric variables in twins with completed craniofacial growth
Bibliographic record
Abstract
OBJECTIVES: To determine genetic and environmental impact on mandibular morphology using lateral cephalometric analysis of twins with completed mandibular growth and deoxyribonucleic acid (DNA) based zygosity determination. MATERIALS AND METHODS: The 39 cephalometric variables of 141 same gender adult pair of twins were analysed. Zygosity was determined using 15 specific DNA markers and cervical vertebral maturation method was used to assess completion of the mandibular growth. A genetic analysis was performed using maximum likelihood genetic structural equation modelling (GSEM). RESULTS: The genetic heritability estimates of angular variables describing horizontal mandibular position in relationship to cranial base and maxilla were considerably higher than in those describing vertical position. The mandibular skeletal cephalometric variables also showed high heritability estimates with angular measurements being considerably higher than linear ones. Results of this study indicate that the angular measurements representing mandibular skeletal morphology (mandibular form) have greater genetic determination than the linear measurements (mandibular size). CONCLUSIONS: The shape and sagittal position of the mandible is under stronger genetic control, than is its size and vertical relationship to cranial base.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".