Avaliação cefalométrica das alterações do perfil facial naturais e induzidas pelos aparelhos de Herbst e Bionator no tratamento da má oclusão de classe II, divisão 1ª
Bibliographic record
Abstract
The aim of this study was to evaluate the facial profile changes due to natural growth and induced by Herbst appliance and Bionator in the treatment of Class II, division 1 malocclusion. In order to do that, we used a sample of 90 lateral radiographs of 45 individuals in pre-pubertal stage, divided up in two experimental groups and one control. The first group, composed of 15 brazilian individuals, with initial mean age of 9.4 years, was treated with the Herbst appliance for a period of seven months. The second experimental group consisting of 15 brazilian individuals, initial mean age of 9.9 years has gone through bionator therapy for an average period of 21 months. The control group of 15 individuals, who were not treated orthodontically, comes up from the Burlington Growth Centre, University of Toronto, Canada. The intragroup comparison was performed using the Student t test and intergroup comparisons by ANOVA complemented by the Bonferroni test. The results have shown that only the group treated with the Herbst appliance presented significant changes in facial profile with improvement of its convexity and lower lip protrusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".