Health effects of identifying patients with undiagnosed obstructive sleep apnea in the preoperative clinic: a follow-up study
Bibliographic record
Abstract
BACKGROUND: Undiagnosed obstructive sleep apnea (OSA) is a highly prevalent breathing disorder. The purpose of this study was to determine the effects of preoperative screening and subsequent treatment for OSA on the health of patients. METHODS: We conducted a two-year follow-up study of patients previously enrolled in a large prospective study in which patients were given the STOP questionnaire for OSA screening (n = 2,467). All patients who underwent a polysomnography were considered eligible (n = 211) and were asked to complete a paper-based mailed questionnaire. The severity of OSA, comorbidities, and treatment modalities and their effects were evaluated from the returned questionnaire. Research ethics board approval was obtained and returning the questionnaire implied informed patient consent. RESULTS: The response rate was 67%. One hundred twenty-eight (82%) of the 156 patients who responded had OSA established by polysomnography. Among these 128 patients with OSA, 88 (69%) were prescribed continuous positive airway pressure (CPAP) therapy and 40 (31%) were prescribed other (non-CPAP) treatment. Among those 88 patients receiving CPAP, 40 (45%) were compliant and 48 (55%) were non-compliant. The CPAP compliant patients had a greater reduction in medication for comorbidities than the CPAP non-compliant or the other treatment group (38% vs 3% vs 0%, respectively; P < 0.001). A significant improvement in snoring, sleep quality, and daytime sleepiness was reported by CPAP compliant users compared with CPAP non-compliant or other treatment groups (P < 0.001). CONCLUSION: The preoperative patients who were identified to have OSA and were compliant with CPAP use may have health benefits in terms of improved snoring, sleep quality, and daytime sleepiness. Timely diagnosis and treatment compliance may reduce symptoms of OSA and severity of associated comorbidities along with a reduction in medications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".