Association between opioid prescription profiles and adverse health outcomes in opioid users referred for sleep disorder assessment: a secondary analysis of health administrative data
Bibliographic record
Abstract
Background: Information is needed to guide safe opioid prescribing in adults referred for a sleep disorder assessment. Previous studies have shown that individuals referred for a sleep disorder assessment have a higher likelihood of long-acting opioids and higher opioid dosages prescription than the general population, suggesting that these individuals are more at risk for opioid-related adverse health consequences. Methods: = 15,713). Through provincial health administrative databases, individuals were followed over time to assess the association between opioid use characteristics and 1-year all-cause mortality, hospitalizations and emergency department (ED) visits, and opioid-related hospitalizations and ED visits within extended follow-up to 2018. Results: Controlling for covariates, chronic opioid use (vs. not) was significantly associated with increased hazards of all-cause mortality [adjusted hazard ratio(aHR): 1.84; 95% confidence interval (CI): 1.12-3.02], hospitalization (aHR: 1.14; 95% CI: 1.02-1.28) and ED visit (aHR: 1.09; 95% CI: 1.01-1.17). A higher opioid dosage [morphine equivalent daily dose (MED) >90 vs. ≤ 90 mg/day] was significantly associated with increased hazards of all-cause or opioid-related hospitalization (aHR: 1.13; 95% CI: 1.02-1.26 and aHR: 2.27; 95% CI: 1.53-3.37, respectively). Morphine or hydromorphone prescription (vs. oxycodone) was significantly associated with an increased hazard of all-cause hospitalization (aHR: 1.30; 1.07-1.59 and aHR: 1.43; 95% CI: 1.20-1.70, respectively). Hydromorphone or fentanyl prescription (vs. oxycodone) was significantly associated with an increased hazard of opioid-related ED visit and/or hospitalization (aHR: 2.28, 95% CI: 1.16-4.47 and aHR: 2.47, 95% CI: 1.16-5.26, respectively). Conclusion: Findings from this retrospective study may inform the safe prescribing of opioids in adults referred for a sleep disorder assessment.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".