Efficiency of oro‐facial myofunctional therapy in treating obstructive sleep apnoea: A meta‐analysis of observational studies
Bibliographic record
Abstract
OBJECTIVE: The literature on oro-facial myofunctional therapy (OMT) in children and adults with obstructive sleep apnoea (OSA) was systematically reviewed to investigate the effects of OMT on patients with OSA by age and disease severity to verify the effect of OMT on OSA. DATA SOURCES: All the comparative literature was retrieved from the PubMed, Embase and Cochrane libraries. METHOD: We searched the articles published up to 12 February 2022 and followed the preferred reporting project for systematic review and meta-analysis of reports. The quality of the studies was evaluated using the Newcastle-Ottawa scale. RESULTS: Of the primary indicators for evaluating OSA, 13 studies reported on the apnoea index (AHI), showing a decrease in the mean standard deviation of AHI from before OMT to after OMT (p < .00001). The lowest oxygen saturation was reported in nine studies, and the mean standard deviation of the lowest oxygen saturation increased from before to after OMT (p = .0009). Ten studies reported the Epworth Sleepiness Scale (ESS), indicating that the mean standard deviation of the ESS decreased from before to after OMT (p < .00001). The subgroup analysis showed that the AHI scores indicating mild and moderate OSA were significantly reduced, and the AHI scores indicating severe OSA also decreased, but this was not statistically significant. The lowest oxygen saturation increased obviously in patients with both mild and moderate and severe OSA. Of the secondary indicators of OSA, there was a statistically significant improvement in snoring intensity (p = .0002). CONCLUSION: Oral and facial muscular function therapy can be used as a simple and non-invasive new technique to improve the AHI, minimum oxygen saturation, ESS, and snoring intensity in patients with mild and moderate OSA and the lowest oxygen saturation in patients with severe OSA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.048 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".