Unpredictable results of laser assisted uvulopalatoplasty in the treatment of obstructive sleep apnoea
Bibliographic record
Abstract
BACKGROUND: Laser assisted uvulopalatoplasty (LAUP) is increasingly offered for the treatment of obstructive sleep apnoea (OSA), although there is a lack of objective data to support its indications and efficacy. A study was undertaken to determine the treatment response to LAUP. METHODS: Overnight polysomnography was performed before and at least three months after surgery in 44 consecutive patients with symptomatic mild to moderate OSA (apnoea + hypopnoea index (AHI) >10/h). Pharyngeal dimensions were measured by videoendoscopy (n = 11) and disease-specific quality of life, sleepiness and snoring frequency (n = 16) before and after surgery were determined in subgroups of patients. LAUP was performed under local anaesthesia as a one stage resection of the uvula and soft palate by one of two experienced otolaryngologists. RESULTS: Twelve patients (27%) had a good response (AHI </=10/h after LAUP); four (9%) had a partial response (AHI </=50% of pre-LAUP value); 15 (34%) had a poor response (AHI >50% of pre-LAUP value); and 13 (30%) patients were worse (AHI >100% of pre-LAUP value). The velopharyngeal cross sectional area and anteroposterior diameter increased following LAUP (p<0.05). Quality of life indices improved significantly in all domains and sleepiness decreased. The snoring index did not decrease significantly. No preoperative anthropometric or videoendoscopic measures were predictive of a good response to LAUP. Patients who were worse after LAUP had milder baseline apnoea severity than those in the other response groups. CONCLUSIONS: The treatment response to LAUP is variable and unpredictable, and only a few patients achieve a satisfactory response. There appears to be no relationship between subjective and objective measures of treatment efficacy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".