A 2-center comparison of maxillary incisor positioning with non-extraction, 2-maxillary premolar and 4-premolar extractions for Class II treatment
Bibliographic record
Abstract
PurposeThe purpose of this study was to evaluate the maxillary incisors position and soft-tissue characteristics of Class II, division 1 patients treated with non-extraction, 2-maxillary and 4-premolar extraction protocols.Materials and methodsThe sample of 120 subjects was divided into four groups. The first three treated groups presented Class II, division 1 malocclusion patients. G1 comprised 30 patients (14.95 years), and was treated without extraction. G2 comprised 30 patients (15.28 years), treated with extraction of 2-maxillary premolars. G3 consisted of 30 patients (15.55 years), treated with extraction of 4-premolars, and G4 included 30 individuals (14.93 years) with normal occlusion. The occlusal features of the treated groups were evaluated by the Peer Assessment Rating (PAR) index and the cephalometric characteristics were compared with ANOVA.ResultsThe maxillary incisors were similarly positioned among the treated groups at the end of treatment. However, they were more upright in G2 and G3 than in the control group (G4). The hard- and soft-tissue cephalometric characteristics were similar in all groups.ConclusionClass II treatment conducted non-extraction, with 2-maxillary and with 4-premolar extraction protocols showed similar effects on final maxillary incisors positioning and nasolabial angle. In general, the 4-premolar extraction protocol was potentially more retrusive, but the dentofacial differences were slight and not statistically significant.
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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) | 0.003 | 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".