Early evidence of the effects of xylazine-adulterated fentanyl in Ohio
Bibliographic record
Abstract
BACKGROUND: Xylazine is becoming a prevalent fentanyl adulterant in the US. It has been associated with severe wounds and withdrawal symptoms. However, its impact on fatal overdose rates is poorly understood. METHODS: Poisson and ordinary least squares regression analyses are used to estimate the relationship between xylazine prevalence and unintentional overdose death and death rates at the county-month level in Ohio from April through December 2023. Xylazine prevalence is calculated from the Ohio Bureau of Criminal Investigation's (BCI) Crime Lab Data, and mortality data is from the Ohio Department of Health. RESULTS: Xylazine prevalence is positively correlated with overdose deaths and death rates in large population counties. Xylazine adulteration is associated with 319 more overdose deaths [95 percent CI: 147-491 deaths], 10 percent of all unintentional overdose deaths in Ohio, over the nine-month period. Our estimates predict that if all fentanyl had been adulterated with xylazine over these nine months, this would have led to an additional 519 deaths. DISCUSSION: Although the data covers a limited time period, our estimates provide evidence that xylazine-adulterated fentanyl is likely to lead to additional overdose deaths as it continues to spread across the US, blunting the initial signs of a declining trend in overdose deaths. If the findings can be extrapolated to the rest of the country, it is likely that overdose deaths would have fallen more substantially in 2023 if xylazine had not already been so prevalent in large parts of the US.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".