Targeting Sleep Disturbance and Sleep Apnea in Patients with Chronic Pain
Bibliographic record
Abstract
Cognitive behavioural therapy for insomnia (CBT-I) may be effective in improving sleep among patients with chronic pain, however, it is largely inaccessible due to high costs, few qualified therapists, and time-consuming nature. We report that brief self-help CBT-I with low-intensity telephone support is feasible and acceptable for patients with chronic pain and sleep disturbance. Some patients may also be prescribed with opioids for pain management, although opioid therapy is associated with a greater prevalence of sleep apnea. Given the higher risk of opioid-associated sleep apnea, it is critical that accessible screening models are created to identify those at risk. We found that a screening model comprising a validated questionnaire and pulse oximetry can accurately screen for moderate-to-severe sleep apnea among patients using opioids for chronic pain. Both studies suggest that low-intensity CBT-I and simple screening models can effectively target sleep disturbance and sleep apnea, respectively, in the chronic pain population.
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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 | 0.001 |
| 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) | 0.004 | 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".