The Relationship between Dentofacial Vertical Pattern and Bite Force Distribution among Children in Late Mixed Dentition
Bibliographic record
Abstract
Background: Digital bite measurement systems such as the T-Scan III allow for the computerized measurement of occlusal force distribution. This study aimed to establish the relationship between dentofacial vertical pattern and bite force distribution among children in late mixed dentition. Materials and Methods: In total, 86 children (45 male, 41 female) aged between 9 and 11 years with short (n = 28), medium (n = 28), and long (n = 30) facial heights were included in this study. The height, weight, age, and gender were recorded. Occlusal bite force distribution and time of occlusal cycle were recorded using a T-Scan III device (Tekscan Corp. Boston, MA, USA). The bite force distribution was compared among facial types using a One-Way ANOVA and post hoc test, a linear regression model with time of occlusion as dependent variable was developed. Results: No significant differences were observed in occlusion time between genders. Children with long facial height had a significantly lower anterior bite force distribution (p < 0.05) and significantly higher posterior bite force distribution (p < 0.05) than those with average or short facial height. Age, gender, height, and weight had no significant association with time of the occlusal cycle. Conclusion: Children with an increased vertical facial height have a more posterior distribution of force than children with average or short facial heights in the late mixed dentition.
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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 | 0.001 |
| 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) | 0.002 | 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".