All-Cause and Overdose Mortality Risk Among People Prescribed Opioids: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To estimate all-cause and overdose crude mortality rates and standardized mortality ratios among people prescribed opioids for chronic noncancer pain and risk of overdose death in this population relative to people with similar clinical profiles but not prescribed opioids. DESIGN: Systematic review and meta-analysis. METHODS: Medline, Embase, and PsycINFO were searched in February 2018 and October 2019 for articles published beginning 2009. Due to limitations in published studies, we revised our inclusion criteria to include cohort studies of people prescribed opioids, excluding those studies where people were explicitly prescribed opioids for the treatment of opioid use disorder or acute cancer or palliative pain. We estimated pooled all-cause and overdose crude mortality rates using random effects meta-analysis models. No studies reported standardized mortality ratios or relative risks. RESULTS: We included 13 cohorts with 6,029,810 participants. The pooled all-cause crude mortality rate, based on 10 cohorts, was 28.8 per 1000 person-years (95% CI = 17.9-46.4), with substantial heterogeneity (I2 = 99.9%). The pooled overdose crude mortality rate, based on six cohorts, was 1.1 per 1000 person-years (95% CI = 0.4-3.4), with substantial heterogeneity (I2 = 99.5%), but indications for opioid prescribing and opioid exposure were poorly ascertained. We were unable to estimate mortality in this population relative to clinically similar populations not prescribed opioids. CONCLUSIONS: Methodological limitations in the identified literature complicate efforts to determine the overdose mortality risk of people prescribed opioids. There is a need for large-scale clinical trials to assess adverse outcomes in opioid prescribing, especially for chronic noncancer pain.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".