Comparative risk of mortality in new users of prescription opioids for noncancer pain: results from the International Pharmacosurveillance Study
Bibliographic record
Abstract
ABSTRACT: Although opioids continue to be used internationally for noncancer pain, evidence to date on the comparative safety of different opioids is sparse and conflicting. The aim of this study was to examine the comparative risk of all-cause mortality in patients newly initiated on opioids for noncancer pain, across 3 jurisdictions in the United Kingdom (UK), United States, and Canada. A multicentre retrospective, population-based cohort study was conducted. Data sources included UK national primary care electronic health records (Clinical Practice Research Datalink), The Partners HealthCare Research Patient Data in Boston (US), and The Montreal Population Health Record data (Canada). New users of opioids aged ≥18 years without cancer were included. Patients with a diagnosis of a pain condition and with known back pain were analysed separately. Fully adjusted hazard ratios (HRs) were calculated using Cox-proportional models and adjusted for confounders. In total, 1,066,216 patients were included (UK: n = 993,294; Boston, US: n = 43,243; Montreal, Canada: n = 26,116). Compared with codeine, patients using morphine had a significantly higher adjusted risk in the UK {HR: 12.58 [95% confidence interval (CI), 11.87-13.32]}, US (HR: 8.62 [95% CI, 3.34-22.27]), and Canadian cohorts (HR: 6.69; [95% CI, 1.35-32.22]). In addition, other factors associated with higher mortality were being on combination opioids, fentanyl, buprenorphine, and oxycodone. Compared with those on <50 morphine milligram equivalents/day, patients on higher-doses experience an incremental increase in risk. In new users of opioids, compared with codeine, strong opioids, including morphine, fentanyl, buprenorphine, oxycodone, and combination opioids, and those on ≥50 morphine milligram equivalent/day were associated with a higher subsequent risk of all-cause mortality.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".