Skeletal changes of maxillary protraction without rapid maxillary expansion
Bibliographic record
Abstract
OBJECTIVE: To determine potential differences in treatment efficiencies of face mask therapy without rapid maxillary expansion (RME) at different early dental stages. MATERIALS AND METHODS: Forty-nine Class III children who were treated with a face mask without RME were divided into two groups according to their pretreatment dental stage. The primary dentition treatment group consisted of 26 subjects and the mixed dentition treatment group consisted of 23 subjects. Lateral cephalograms before treatment (T0), at the end of treatment (T1), and at least 1 year after the end of treatment (T2) were calculated and analyzed. Fourteen cephalometric variables were evaluated by t-test to identify any significant differences in skeletal changes between the two groups during T1-T0, T2-T1, and T2-T0. RESULTS: The primary dentition group showed not only a greater response to maxillary protraction without RME than did the mixed dentition group during T1-T0, but also a greater relapse tendency during T2-T1. As a result, no significant differences were noted between the two groups in the treatment effects of face masks without RME over the time period T2-T0. CONCLUSION: This study suggests that face mask therapy without RME may be postponed to the early to mid mixed dentition period because the therapy induces similar skeletal changes when initiated at primary or mixed dentition.
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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 | 0.002 |
| 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".