The Effect of Prescription Opioid Injection on the Risk of Non-Fatal Overdose Among People Who Inject Drugs
Bibliographic record
Abstract
Objectives—Prescription opioid (PO) use by people who inject drugs (PWID) is a growing public health concern. Non-fatal overdose remains a leading source of morbidity among PWID, however, little is known about the relationship between PO injection and non-fatal overdose in this population. In this study we sought to examine the impact of PO injection on non-fatal overdose among PWID from Vancouver, Canada. Methods—Data were derived from two open prospective cohorts of PWID for the period of December, 2005 to May, 2014. Multivariable generalized estimating equations were used to examine the odds of overdose among those who injected: POs; heroin; and POs and heroin. Results—In total, 1660 PWID (33.7% women) participated in this study. In multivariable analyses, in comparison to those who were injecting non-opioid drugs, exclusive PO injection was not significantly associated with non-fatal overdose (adjusted odds ratio [AOR]: 1.17, 95% confidence interval [CI]: 0.74 – 1.86). The odds of non-fatal overdose were elevated for heroin injection (AOR: 1.72, 95% CI: 1.31 – 2.27), but were greatest for those who injected both heroin and POs (AOR: 2.46, 95% CI: 1.83 – 3.30).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".