Drug type and risk behaviors associated with non-fatal overdose among people who use drugs: a systematic review and meta-analysis
Bibliographic record
Abstract
The present study aimed to determine the association between drug type, risk behaviors and non-fatal overdose among people who use drugs (PWUD). We searched for studies in English published before February 1, 2021, on PubMed, Scopus, Cochrane, and Web of Science to identify primary studies on the factors associated with non-fatal overdose among PWUD. After reviewing for study duplicates, the full-text of selected articles were assessed for eligibility using Population, Intervention, Comparator, Outcomes (PICO) criteria. After a detailed assessment of over 13,845 articles, a total of 49 studies met the eligibility criteria. We found that non-injection opioid use, heroin injection, cocaine use, concurrent use of buprenorphine and benzodiazepines, benzodiazepine use, incarceration, injecting drugs, and duration of injecting were associated with greater odds of non-fatal overdose among PWUD. The findings of the current meta-analysis support the requirement to improve suitable harm reduction strategies for drug users, such as peer-based overdose management, and further focusing on the need to balance the current emphasis on enforcement-based responses to illegal drug use with health-related interventions.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".