Reduction in mortality risk with opioid agonist therapy: a systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Opioid agonist therapies are effective medications that can greatly improve the quality of life of individuals with opioid use disorder. However, there is significant uncertainty about the risks of cause-specific mortality in and out of treatment. OBJECTIVE: This systematic review and meta-analysis explored the association between methadone and buprenorphine with cause-specific mortality among opioid-dependent persons. METHODS: We searched six online databases to identify relevant cohort studies, calculating all-cause and overdose-specific mortality rates during periods in and out of treatment. We pooled mortality estimates using multivariate random effects meta-analysis of the crude mortality rate per 1000 person-years of follow-up as well as relative risks comparing mortality in vs. out of treatment. RESULTS: A total of 32 cohort studies (representing 150 235 participants, 805 423.6 person-years, and 9112 deaths) met eligibility criteria. Crude mortality rates were substantially higher among methadone cohorts than buprenorphine cohorts. Relative risk reduction was substantially higher with methadone relative to buprenorphine when time in-treatment was compared to time out-of-treatment. Furthermore, the greatest mortality reduction was conferred during the first 4 weeks of treatment. Mortality estimates were substantially heterogeneous and varied significantly by country, region, and by the nature of the treatment provider. CONCLUSION: Precautions are necessary for the safer implementation of opioid agonist therapy, including baseline assessments of opioid tolerance, ongoing monitoring during the induction period, education of patients about the risk of overdose, and coordination within healthcare services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".