Profile changes in orthodontic patients following mandibular advancement surgery
Bibliographic record
Abstract
Purpose: To define initial hard and soft tissue convexity necessary for profiles to consistently improve after mandibular advancement and to assess if pre-surgical lower incisor inclination (IMPA) affects profile change. Methods: 20 general public, 20 orthodontists, and 20 oral surgeons used a Likert scale to rate attractiveness of before and after treatment profiles of mandibular advancement patients. Spearman’s correlation tested for relationships between amount of profile change and varying ANB, profile angle and pre-surgical IMPA. Wilcoxon test compared extraction and non-extraction profile changes. Results: There was a tendency for inverse correlations between profile change and profile angle, but was not statistically significant any of the 3 groups. There was a tendency for positive correlations between profile change and ANB, but was considered significant only for orthodontists. Orthodontists, oral surgeons and the general public found profiles consistently improved when profile angles were ≤159º, ≤158º and ≤157º, respectively. Orthodontists and oral surgeons found profiles consistently improved when ANB angles were ≥5.5º and ≥6.5º, respectively. Profile worsening increases 2.6 to 5.0 times when profile angles exceeded thresholds, and 4.5 to 7.9 times when ANB angles were less than thresholds. No difference in IMPA or profile change in extraction and non-extraction groups. Conclusion: Extractions are not predictive of a greater surgical profile change. Pre-treatment profile angles <160º and ANB >6º are necessary for consistent improvements after surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".