Opioid use and initiation of positive airway pressure treatment in adults referred for sleep disorder assessment: An explanatory population-based study
Bibliographic record
Abstract
OBJECTIVES We conducted a retrospective population-based study to explore: (i) the prevalence of opioid use among adults referred for a sleep disorder assessment and (ii) the relationship between opioid use and initiation of positive airway pressure (PAP) treatment.METHODS All adults who underwent an initial diagnostic sleep study (index date) in Ontario (Canada) between 2013 and 2016 were identified through provincial health administrative data. Opioid use was identified as active or recent opioid prescriptions (at or close to the index date) and being on opioids at any time in the last year. Cause specific hazard models were utilized.RESULTS Of 268,293 adults, about 5% had an active or recent opioid prescription, 25% were on opioids in the last year, and 39% initiated PAP in a median of 17.4 months. Among active opioid users, 12% were treated with a daily dose ≥200 mg morphine equivalent, 37% were treated with long-acting opioids, and 38% were on benzodiazepines in the last year. Only opioid use in the last year was significantly associated with PAP initiation (1.08; 1.06–1.09), but not an active or recent opioid prescription. Among active opioid users, long-acting opioid users and those on high opioid daily dosage were less likely to initiate PAP.CONCLUSIONS We found a high degree of prior opioid exposure among people referred for a sleep disorder assessment with a large proportion on long-acting opioids, higher opioid dosages and on benzodiazepines among active opioid users. However, active opioid treatment was not associated with a higher likelihood of PAP initiation; active opioid users at higher risk of impaired breathing in sleep were less likely to initiate PAP.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".