Overdose Risk and Acquiring Opioids for Nonmedical Use Exclusively from Physicians in Vancouver, Canada
Bibliographic record
Abstract
Background A primary response to the alarming rise in overdose and mortality due to nonmedical prescription opioid (PO) use has been to restrict opioid prescribing; however, little is known about the relationship between obtaining opioids from a physician and overdose risk among people who use POs nonmedically and illicit street drugs. Objectives: Investigate the relationship between non-fatal overdose and acquiring POs exclusively from physicians for the purposes of engaging in nonmedical PO use. Methods: Data were collected between 2013 and 2016 among participants in two harmonized prospective cohort studies of people who use drugs in Vancouver: the At-Risk Youth Study (ARYS) and the Vancouver Injection Drug Users Study (VIDUS). Analyses were restricted to participants who engaged in nonmedical PO use and used generalized estimating equations. Results: Among 599 participants who used POs nonmedically, 82 (14%) individuals reported acquiring POs exclusively from a physician and 197 (33%) experienced a non-fatal overdose at some point over the study period. Acquiring POs exclusively from physicians was significantly and negatively associated with non-fatal overdose in the bivariate analysis (Odds Ratio = 0.60, 95% Confidence Interval (CI): 0.39–0.94) but not the final multivariate analysis (Adjusted Odds Ratio =0.87, 95% CI: 0.53–1.44). Conclusions: Compared to individuals who acquired POs from friends or the streets, participants who acquired POs exclusively from a physician were not at an increased risk of non-fatal overdose. Although responsible opioid prescribing is an important priority, additional strategies to address nonmedical PO use are urgently needed to reduce overdose and related morbidity and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".