Effectiveness of Maxillo-Mandibular Advancement on Obstructive Sleep Apnea—A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The review was performed to evaluate effect of maxillo-mandibular advancements (MMA) on patients with obstructive sleep apnea (OSA) for long term and subjective outcomes. Review was adhered to PRISMA guidelines 2020. Articles screening was done independently by two authors. Quality assessment was done through Newcastle Ottawa scale (NOS) and Cochrane risk of bias (ROB)-2 tool. Standardized mean difference (SMD) was used as summary statistic measure employing random effect model through Review manager (RevMan) version 5.3. Twelve studies (5 retrospective studies, 4 prospective studies and 3 clinical studies) were included for qualitative synthesis and ten studies for meta-analysis. Included studies evaluated subjective parameters like apnea index (AI), Apnea-Hypopnea Index (AHI), Desaturation Index (DI), Epworth Sleepiness Scale (ESS), Hypopnea index (HI), Respiratory Disturbance Index (RDI), Total Sleep Time (TST) and airway morphological parameters like Posterior Airway Space (PAS). Meta-analysis was conducted on AHI, mean SpO2, RDI, TST, ESS and airway morphological parameters like Al and PAS which indicated that post treatment witgh MMA had improved the overall quality of life (P<0.05). Included studies had presence of low to moderate risk of bias. No asymmetry was seen on funnel plot signifying absence of publication bias in meta-analysis. It was found that MMA is an ideal, reliable and most effective treatment modality for patients with OSA improving their overall quality of life with minimal complications with high success and cure rate.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.006 | 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) | 0.004 | 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".