Fentanyl concentrations in unregulated opioids and the blood of drug toxicity decedents
Bibliographic record
Abstract
Abstract Fentanyl toxicity is the leading cause of unnatural death in British Columbia (BC), Canada, driven in part by fentanyl’s potency and unpredictable, highly variable concentration in unregulated drug samples. This study aims to identify emerging trends and the possible relationship between fentanyl concentrations in community drug samples and the postmortem blood of drug-toxicity decedents. Data for this ecological study were derived from Vancouver, BC community drug-checking sites and the BC Coroners Service. We analyzed fentanyl concentrations of opioid drug-checking samples alongside postmortem blood fentanyl concentrations from unintentional drug-toxicity decedents in which fentanyl contributed. The study period was January 2018 through December 2022. Monthly median fentanyl concentrations of drug-checking samples were compared to postmortem blood concentrations using generalized additive models. The primary time-series model, adjusted for potential confounders, did not identify a statistically significant association between drug-checking and postmortem concentrations (edf = 3.58, χ2 = 8.48, P = 0.104); however, a sensitivity analysis excluding extreme values revealed a significant nonlinear association (edf = 3.91, χ2 = 11.71, P = 0.038). Fentanyl concentrations in both the unregulated drug supply and postmortem blood changed over time with the two being mutually associated: as median fentanyl concentrations in opioid drug-checking samples increased, postmortem blood fentanyl concentrations generally decreased. Further research into complexities of the drug supply, changing substance use patterns, and rates of fentanyl exposure among opioid-naïve individuals may explain this nonlinear trend and inform strategies to abate the ongoing crisis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".