Laser-Assisted Uvulopalatoplasty for the Treatment of Snoring and Mild Obstructive Sleep Apnea Syndrome
Bibliographic record
Abstract
OBJECTIVE: To determine the efficacy of the laser-assisted uvulopalatoplasty (LAUP) procedure on snoring and Apnea-Hypopnea-Index (AHI) improvement in patients with snoring and mild obstructive sleep apnea syndrome (OSAS). STUDY DESIGN: Prospective, nonrandomized, nonblinded assessment of outcomes after LAUP in patients suffering from benign habitual snoring and/or mild OSAS. METHODS: Fifty-nine patients with habitual snoring and 25 patients with mild OSAS underwent LAUP (6 of them underwent simultaneous classic tonsillectomy and 20 carbon-dioxide laser tonsillotomy). All patients and their bed partners completed pre- and post-treatment questionnaires ranking snoring, whereas the patients with mild OSAS underwent postoperative polysomnography (PSG). RESULTS: During a 6-month to 5-year follow-up (mean 40 months), 91.5% of the patients with habitual snoring reported significant short-term improvement based on post-treatment questionnaires, whereas 79.7% reported long-term subjective improvement. Nineteen of 25 patients (76%) with mild OSAS reported significant improvement of snoring based on posttreatment questionnaires. According to the postoperative PSG, only 2% showed a worse AHI, whereas 60% showed reduction of the AHI to < or = 5. Eight patients (32%) showed little or no improvement of AHI. CONCLUSIONS: LAUP, in combination with carbon-dioxide laser tonsillotomy in some cases, is a safe, cost-effective, outpatient procedure for the treatment of many cases of habitual snoring and mild OSAS when preceded by careful selection of the candidates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".