Maxillary molars in class II, derotate or distalize: A Systematic review
Bibliographic record
Abstract
Purpose: This review’s aim is to prove whether derotation can correct minimal class II. Materials and Methods: The systematic search included Medline (PubMed, Ovid MEDLINE and EBSCO, Science Direct, and Cochrane Library (Cochrane Review, Trails), and additional studies were searched in the reference lists of all articles. The date of the last search was December 13th, 2022. The methodological quality of the retrospective studies were graded by means of the Quality Assessment Tool for Quantitative Studies, developed for the Effective Public Health Practice Project (EPHPP), and prospective studies by means of the Newcastle–Ottawa Scale. Results: Totally, 1342 studies were identified for screening, and 5 studies were eligible. The Quality Assessment Tool for Quantitative Studies rated 2, of the included retrospective clinical studies as high risk and 1 as moderate risk. The Newcastle–Ottawa Scale rated all 2 included studies as high risk. The mean molar derotation values varied from 1 mm to 2 mm. Conclusion: Through this systematic review, we have highlighted that; the derotation can correct the minimal class II. It is possible thanks to several devices like traspalatin arch, clear aligner, headgear, and some distalizers especially those with vestibular action. The mean molar derotation values varied from 1 mm to 2 mm, conditionally to not lose the space obtained by the effect of medialization.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".