Risk of motor vehicle collisions after methadone use: A systematic review and meta-analysis
Bibliographic record
Abstract
Methadone maintenance therapy is a leading treatment strategy for stabilizing and rehabilitating patients with opioid dependence; however, findings related to the risk of motor vehicle collisions after methadone use have been conflicting. In the present study, we compiled the available evidence on the risk of motor vehicle collisions after methadone use. We completed a systematic review and meta-analysis of studies identified on six databases. Two reviewers independently screened the identified epidemiological studies, extracted data, and used the Newcastle–Ottawa Scale to assess the quality of the studies. Risk ratios were retrieved for analysis, conducted using random-effects model. Sensitivity analyses, subgroup analyses, and tests for publication bias were conducted. Among 1446 identified relevant studies, a total of 7 epidemiological studies enrolling 33226142 participants met the inclusion criteria. Overall, study participants with methadone use had a higher risk of motor vehicle collisions than did those without methadone use (pooled relative risk 1.92, 95% CI 1.25–2.95; number needed to harm 11.3, 95% CI 5.3–41.6); the I2 statistic was 95.1%, indicating substantial heterogeneity. Subgroup analyses revealed that database type explained 95.36% of the between-study variance (p = 0.008). Egger’s (p = 0.376) and Begg’s (p = 0.293) tests revealed no evidence of publication bias. Sensitivity analyses indicated that the pooled results were robust. The present review revealed that methadone use is significantly associated with a nearly doubled risk of motor vehicle collisions. Therefore, clinicians should exercise caution in implementing methadone maintenance therapy for drivers.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.048 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".