Sleep and daytime sleepiness problems among patients with chronic noncancerous pain receiving long-term opioid therapy: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: Sleep problems are common among patients with chronic pain (CP). Information on sleep problems and associated covariates in opioid-treated patients with CP is limited. The aim of this study was to assess the prevalence, characteristics, and risk factors of sleep and daytime sleepiness problems in this specific population. DESIGN: Cross-sectional. SETTING: Primary care outpatient clinics. PARTICIPANTS: Eight hundred and seventy six patients with CP treated with long-term opioids. MAIN OUTCOME MEASURES: Prestudy selected questionnaires: six questions from the Medical Outcomes Study Sleep Scale, Pain Inventory Survey, Pain Patient Profile, Substance Dependence Severity Scale, and medication log. RESULTS: Insomnia-type sleep problems and combined sleep and sleepiness problems were reported by 87 percent and 49 percent of the sample, respectively. Logistic regression analysis showed that depression (adjusted OR, aOR 2.8, 95% CI2.1-3.7) and pain severity (aOR 1.4, 95% CI 1.1-1.7) were the strongest independent predictors of sleep problems; only depression severity predicted daytime sleepiness (aOR 1.9, 95% CI 1.6-2.2) or combined sleep/sleepiness problems (aOR 2.2, 95% CI 1.8-2.5). Opioid dose was associated with a slight tendency toward unrefreshing sleep (aOR 1.2, 95% CI 1.0-1.4) and worse sleep maintenance (aOR 1.2, 95% CI 1.0-1.4), while use of long-acting opioids was associated with a trend toward increased napping (aOR 1.3, 95% CI 1.0-1.8). CONCLUSIONS: Sleep and daytime sleepiness problems are common among opioid-treated primary care patients with CP and seem to be related mainly to depression and pain severity. Physicians caring for opioid-treated patients with CP may want to assess them for sleep disorders as a part of routine CP care.
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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 | 0.002 | 0.000 |
| 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) | 0.000 | 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".