Dimensional Airway Changes due to the Use of Fixed Functional Appliances in Class II Malocclusion in Growing Patients: A Systematic Review
Bibliographic record
Abstract
Abstract Aim: To determine the dimensional changes of the airways with the use of fixed functional appliances (FFA) in growing individuals with Class II malocclusion. Materials and Methods: The search was performed in five electronic databases (PubMed, Scopus, Embase Cochrane Library, and LILACS) until April 2022. Volume, area, or distance measurements of the global airway or by retropalatal (RP), nasopharynx (NP), oropharynx (OP), hypopharynx, and laryngopharynx (LP) regions were analyzed with intragroup or intergroup radiographs versus fixed orthodontic treatment (FOT) or no treatment (NT). Risk of bias was assessed with the Newcastle–Ottawa scale. Results: Out of 754 articles, six cohort studies were included. At least one airway parameter was found to increase with FFA, with better results in the oropharyngeal airway. In six studies, airway dimensions significantly increased with Herbst with or without FOT (↑12.9%–139.2%), Forsus with FOT (↑28.7%) and mandibular anterior repositioning appliance (MARA) with FOT (↑22.6%–61%), with five studies reporting significant results ( P < 0.05). The increase was in volume (14.8%–139.2%), areas (24.1%–61%), and distances (12.9%–39.3%) and in the RP (26.2%–100.7%), NP (7.8%–20.4%), OP (5.3%–139.2%), and LP (24.8%–94.7%) regions. Four cohorts showed significantly higher values of OP and LP volume and distance and cross-sectional areas with Herbst with or without FOT and MARA with FOT compared to FOT or NT ( P < 0.05). Low risk of bias was more frequent ( n = 4) than high risk ( n = 2). Conclusion: Airway measurements were not standard between studies, thus limiting the group comparison. Various measurements of airway regions limit intergroup comparisons, and thus, a standard protocol is needed in future studies. Herbst, MARA, and Forsus FFAs used in growing patients with Class II malocclusion and showed benefits in the airways, especially by increasing the volume of OP in comparison to NT or FOT.
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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 | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".