Prevalence and characteristics of pain in moderate-to-severe obstructive sleep apnea patients and effect of CPAP treatment
Bibliographic record
Abstract
Pain problems are common in patients with obstructive sleep apnea (OSA), but few studies have thoroughly evaluated pain in these patients. The objective of this study was to examine the prevalence and characteristics of pain in moderate-to-severe OSA patients and the effect of continuous positive airway pressure (CPAP) treatment. Moderate-to-severe OSA patients and healthy controls (HC) completed the Short Form McGill Pain Questionnaire (SF-MPQ) and a portion of the Brief Pain Inventory (BPI) Short Form to assess pain characteristics. The Epworth Sleepiness Scale (ESS), the Short Form-36 (SF-36), and the Hospital Anxiety and Depression Scale (HADS) were used to measure daytime sleepiness, health-related quality of life (HRQoL), and psychological status, respectively. The OSA patients with pain were divided into a CPAP-treated group and a CPAP-untreated group based on their adherence to CPAP. The subjects' pain intensity was reassessed after 3 months. The prevalence of pain was 57.5% in OSA versus 27.1% in HC (p < 0.001). Head (39.0%) accounted for the highest proportion of overall pain locations in subjects with OSA, with 28.8% of OSA patients experiencing headaches. Pain in OSA was associated with impaired HRQoL and psychological problems. Patients with very severe OSA had an increased risk for pain problems (OR: 7.000, p = 0.041). Associated factors for pain intensity in OSA included age, ESS ≥ 9.0, and lowest pulse oximetry (LSpO2) < 80.0%. Pain intensity in OSA decreased significantly after CPAP treatment (p < 0.001). Pain was prevalent among patients with moderate-to-severe OSA and was associated with depression, anxiety, and a lower HRQoL. Patients with very severe OSA had an increased risk for pain problems. The intensity of pain in OSA can be predicted by age, ESS ≥ 9.0, and LSpO2 < 80.0%, and it can be alleviated through CPAP treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".