How substance use preferences and practices relate to fentanyl exposure among people who use drugs in Rhode Island, USA
Why this work is in the frame
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Bibliographic record
Abstract
Over 107,000 people died in the United States (U.S.) from drug overdose in 2022, with over one million overdose deaths since 1999. The U.S. drug market is characterized by a highly toxic, unregulated, and rapidly changing supply. Understanding the extent of exposure to fentanyl among people who use drugs (PWUD) will guide public health interventions aimed to decrease overdose. We utilized baseline data from the Rhode Island Prescription and Illicit Drug Study, a randomized controlled trial of harm reduction-oriented interventions for PWUD in Rhode Island from 2020 to 2023. We evaluated sociodemographic and drug use-related covariates and examined fentanyl presence in urine drug testing (UDT). We built a classification and regression tree (CART) model to identify subpopulations with the highest likelihood of fentanyl presence in UDT. Among 446 participants, those with fentanyl present in UDT tended to be younger, non-Hispanic white, and had recently injected drugs ( p <0.05 for all). The CART analysis demonstrated a large variation in sample sub-groups’ likelihood of fentanyl presence in UDT, from an estimated probability of 0.09 to 0.90. Expected recent fentanyl exposure was the most important predictor of fentanyl in UDT. Univariate analyses and CART modeling showed substantial variation in the presence of fentanyl in UDT among PWUD. Harm reduction services for people actively injecting drugs and drug checking programs based on capacity-building, empowerment, and targeted towards those not yet engaged in services are urgently needed to support PWUD in navigating the current volatile drug supply. • Fentanyl preference and confidence about exposure predict fentanyl presence. • Younger age, male sex at birth, injection drug use is associated with fentanyl presence. • Results show substantial variation in the presence of fentanyl among PWUD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it