Anterior palatoplasty in the treatment of obstructive sleep apnoea - a systemic review
Bibliographic record
Abstract
This study seeks to determine the objective and subjective success rates of anterior palatoplasty and its variants for the treatment of obstructive sleep apnoea (OSA). A systematic review with two independent searches of Medline, PubMed, Cochrane Library and Evidence Based Medicine Reviews was performed to identify publications relevant to OSA and anterior palatoplasty. All relevant studies published before 30 June 2015 were included. Six studies were included in the systematic review. The numbers of patients in each paper ranged from 13 to 77 (total = 240), and mean age ranged from 21 to 51 years. Substantial and consistent improvement in polysomnography (PSG) outcomes were observed in patients after the anterior palatoplasty, with or without multilevel surgery. The results showed that the anterior palatoplasty technique provides significantly improvement in the post-operative Apnoea-Hypopnea Index. The mean pre-operative AHI (in the 6 papers) improved from 16.3 to 7.1, the snore visual analogue scale improved from 7.5 to 3.1 and the Epworth score reduced from 11.3 to 7.3 post-operatively. The overall pro-rated pooled success rate for all the patients was 72.5%, with a mean follow-up of 17.3 months. Anterior palatoplasty is an effective option in the management of patients with mild to moderate 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".