Effects of Continuous Positive Airway Pressure on Quality of Life in Patients With Moderate to Severe Obstructive Sleep Apnea: Data From a Randomized Controlled Trial
Bibliographic record
Abstract
STUDY OBJECTIVES: Previous studies have shown that CPAP has a substantial impact on daytime symptoms and quality of life (QOL). It remains unclear which outcome measures best identify real CPAP effects and carry independent information. METHODS: One hundred-two men with moderate-severe obstructive sleep apnea were randomized to either "real" or "sham" CPAP for one month. Outcome measures were subjective sleepiness (Epworth Sleepiness Scale [ESS]) and QOL measures includiig SF-36/SF-12 and Calgary Sleep Apnea Quality of Life Index (SAQLI). The bed partner's QOL and rating of patient's response to CPAP were assessed with the Dublin questionnaire. All data were standardized using effect sizes and expressed as real minus sham to remove the nonspecific effects of placebo. RESULTS: Real CPAP was superior to sham CPAP in almost all outcome measures. ESS, patient's component from Dublin, and social interactions from SAQLI showed the largest differences in effect sizes between real and sham (1.33, 0.98, and 0.92 respectively). ESS carried the highest predictive power of real CPAP response (P < 0.0001, r2 = 0.21). Question number 5 from Dublin (partner assessed patient's sleep quality) and question 6 from ESS (dozing while talking) were the best single item predictors of real CPAP response. CONCLUSIONS: Real CPAP reduces subjective sleepiness and improves QOL of both patients and bed partners. ESS is the best score; question number 5 from Dublin and question number 6 from ESS are the best single item predictors of real CPAP response. This information should allow the selection of appropriate questions in clinical practice and research protocols.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".