Mandibular advancement appliances for the treatment of paediatric obstructive sleep apnea: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of mandibular advancement appliances (MAAs) for treatment of pediatric obstructive sleep apnea (OSA). METHODS: Several electronic databases (The Cochrane Database, EMBASE, Healthstar, MEDLINE, PubMed) were systematically searched, as well as a limited grey literature (Google Scholar) and manual searches. A health sciences librarian helped with the selection of Medical Subject Headings (MeSH), key words, and combinations of key words with truncations to account for any differences in controlled terminology in the different databases. Only studies that evaluated the effects of MAAs in children with OSA were pursued. RESULTS: Only 4 articles satisfied all inclusion criteria. Selected studies were retrospective except one study that was a quasi-randomized clinical trial. High risk of bias (Cochrane Risk of Bias assessment) was judged in all included studies. Based on the limited available evidence use of MAAs in a POSA population may result in improvements in Apnea Hypopnea Index (AHI) scores. However complete normalization of AHI scores was not demonstrated. Heterogeneity in study designs and collected information precluded meta-analysis. LIMITATIONS: There are significant weaknesses in the existing evidence due primarily to absence of control groups, small sample sizes, lack of randomization and short-term results. Determination of AHI scores with MAAs still in the mouth should be avoided. CONCLUSIONS: The current limited evidence may be suggestive that MAAs result in short-term improvements in AHI scores, but it is not possible to conclude that MMAs are effective to treat pediatric OSA. Medium- and long-term assessments are still required.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".