Impact of functional orthodontic treatment on facial attractiveness of children with Class II division 1 malocclusion
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Whether orthodontic treatment with functional appliances improves facial aesthetics is still under debate. This study aimed to determine whether functional orthodontic treatment improves the facial attractiveness of patients with Class II division 1 malocclusion. MATERIALS/METHOD: Extraoral lateral photographs of 20 children with Class I (CLI, 11.7 ± 0.8 years), and before (T1) and after treatment (T2) photographs of patients with Class II division 1 (CLII T1 and CLII T2; mean age ± SD = 11.1 ± 0.6 years) treated with functional appliances, were transformed into black silhouettes. Three panels of examiners including 30 orthodontists (39.0 ± 10.1 years), 30 dentists (40.0 ± 9.7) and 30 laypersons (39.0 ± 9.2) evaluated the attractiveness of patients' silhouettes using a 100-mm visual analogue scale, and the sagittal position of patients' upper lip, lower lip, and chin using a 3-point Likert scale. Two-way ANOVA and a chi-square test were used to test differences among groups. Statistically significance was set as P < 0.05. RESULTS: The silhouettes of CLII T2 individuals were more attractive than those of the other groups (all Ps < 0.001). The upper lip, lower lip, and the chin of these individuals were judged to be normally positioned in 69.5 per cent, 74.9 per cent, and 72.3 per cent of the assessments, respectively (all Ps < 0.05). LIMITATIONS: This study did not account for the psychological profile of the examiners, which may have affected the ratings. CONCLUSIONS/IMPLICATIONS: Orthodontic treatment with functional appliances is associated with a superior facial profile attractiveness. Functional treatment should be considered as a treatment option to improve the facial appearance of children with Class II division 1 malocclusion.
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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 | 0.000 | 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) | 0.004 | 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".