Cephalometric changes in Class II division 1 patients treated with two maxillary premolars extraction
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the cephalometric alterations in patients with Angle Class II division 1 malocclusion, orthodontically treated with extraction of two maxillary premolars. METHODS: The sample comprised 68 initial and final lateral cephalograms of 34 patients of both sex (mean initial age of 14.03 years and mean final age of 17.25 years), treated with full fixed appliances and extraction of the first maxillary premolars. In order to evaluate the alterations due the treatment between initial and final phases, the dependent t test was applied to the studied cephalometric variables. RESULTS: The dentoskeletal alterations due to extraction of two maxillary premolars in the Class II division 1 malocclusion were: maxillary retrusion, improvement of the maxillomandibular relation, increase of lower anterior facial height, retrusion of the maxillary incisors, buccal inclination, protrusion and extrusion of the mandibular incisors, besides the reduction of overjet and overbite. The tissue alterations showed decrease of the facial convexity and retrusion of the upper lip. CONCLUSIONS: The extraction of two maxillary premolars in Class II division 1 malocclusion promotes dentoskeletal and tissue alterations that contribute to an improvement of the relation between the bone bases and the soft tissue profile.
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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) | 0.003 | 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".