Time-Series Analysis of Fentanyl Concentration in the Unregulated Opioid Drug Supply in a Canadian Setting
Bibliographic record
Abstract
North America has been contending with an unregulated street drug supply in which opioids are often adulterated with illicitly manufactured fentanyl. The unpredictability of composition may result in an increased risk of overdose due to unexpected elevated concentrations of the high-potency drug. Using data from a community-based drug-checking project, we evaluated trends in fentanyl concentration of illicit opioids in the context of an overdose epidemic. Using a quantification model for fentanyl hydrochloride, historical Fourier-transform infrared spectra from opioid drug-checking samples were analyzed to determine fentanyl concentrations. Median monthly fentanyl concentrations were plotted, and polynomial and autoregressive time-series analyses were performed to examine trends over time. A total of 3,621 fentanyl-positive samples were included in the study, spanning November 2017 to December 2019. Monthly median fentanyl concentrations ranged from 4.5% to 10.4%. Time-series analyses indicated that a third-degree polynomial model fit the data well (R2 = 0.639), suggesting a cyclical pattern in median concentration over time. Notably, absolute variance in fentanyl concentration decreased by an average 0.1% per month (P < 0.001). Future research should explore the relationship between fentanyl concentration and overdose to identify potential targeted harm-reduction interventions that can respond to changes in observed fentanyl concentration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".