Effectiveness of open bite correction when managing deleterious oral habits in growing children and adolescents: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Oral habits are common etiological factors for anterior open bites (AOBs) in growing children and adolescents. The objective of this review was to provide a literature synthesis evaluating the effectiveness of open bite correction in growing individuals with the use of habit-interception appliances. SEARCH METHODS: Electronic searches were conducted on PubMed, Embase, Cochrane Library, Web of Sciences, Scopus, Google Scholar, Scielo, and Lilacs databases. Trials registries were consulted for ongoing trials, and a partial grey literature search was also conducted. SELECTION CRITERIA: The selection criteria included controlled clinical trials enrolling growing subjects who underwent habit-interception orthodontic treatment to correct dental and/or skeletal AOB. DATA COLLECTION ANALYSIS: Data was grouped and analysed descriptively. A meta-analysis was only possible regarding crib therapy effectiveness. Qualitative appraisal was performed according to Cochrane Risk of Bias tool for randomized clinical trials (RCTs) and the MINORS tool for non-randomized clinical trials (nRCTs). RESULTS: Two RCTs and nine nRCTs were identified. Most of them presented relevant limitations. Crib therapy demonstrated to be effective (+3.1mm overbite correction). However, most of the dental effects are seemingly lost with time; and the skeletal effects are still controversial. Other habit-interception appliances, such as spurs, were not sufficiently investigated. CONCLUSIONS: Crib therapy appears to be effective on a short time basis. As for other habit-interception appliances, insufficient evidence could not provide reliable conclusions.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".