Prognostic Factors for Successful Functional Appliance Therapy in Skeletal Class II Malocclusion
Bibliographic record
Abstract
AIM: To investigate clinical or cephalometric parameters that contribute to favourable outcomes with functional appliance therapy in skeletal class II malocclusion. MATERIALS AND METHODS: Six electronic databases were searched PubMed, Ovid, Lilacs, Cochrane, Scopus, and Web of Science up to 25thFebruary 2025. All study designs which evaluated factors associated with favourable and unfavourable outcome with functional appliance therapy for the treatment of skeletal class II malocclusion were included. The electronic search, initial screening, data extraction, risk of bias assessment was independently performed by the two reviewers. The collected data were analysed from the finally selected articles based on type of study, sample size, type of functional appliance used, and patient characteristics or factors studied which could be considered as positive predictive factors for functional appliance. RESULTS: Seven retrospective studies and one prospective study were included. Prognostic factors like the Co-Go-Me angle, chin position, growth pattern and other occlusal factors such as overbite, overjet were evaluated in the selected studies. Four studies were rated very good with 9 points and four other studies were rated as satisfactory with 7 points using the New Castle Ottawa Scale. Due to heterogeneity of the factors studied a meta–analysis could not be conducted in this systematic review. CONCLUSION: Two included studies reported Co- Go-Me angle as the single most important predictive factor for successful outcome. Cephalometric factors pointing to horizontal growth pattern or hypodivergence, chin position and occlusal variables like increased overbite and overjet were identified as positive predictive factors. However future studies with definitive comparison groups can strengthen the current evidence. REGISTRATION: PROSPERO(CRD42022312039).
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".