The Detection of Xylazine in Tijuana, Mexico: Triangulating Drug Checking and Clinical Urine Testing Data
Bibliographic record
Abstract
INTRODUCTION: Xylazine is a veterinary anesthetic increasingly present alongside illicit fentanyl in the United States and Canada, presenting novel health risks. Although xylazine remains less common in the Western US, Mexican border cities serve as key trafficking hubs and may have a higher prevalence of novel substances, but surveillance there has been limited. METHODS: We examined deidentified records from the Prevencasa free clinic in Tijuana, describing urine and paraphernalia testing from patients reporting using illicit opioids within the past 24 hours. Xylazine (Wisebatch and Safelife brands), fentanyl, opiate, methamphetamine, amphetamine, benzodiazepine, and nitazene test strips were used to test urine and paraphernalia samples. Paraphernalia samples were also analyzed with mass spectrometry. RESULTS: Of n=23 participants providing urine and paraphernalia samples concurrently, 100%, 91.3%, and 69.6% reported using China White/fentanyl, methamphetamine, and tar heroin, respectively. The mean age was 41.7 years, 95.7% were male, 65.2% were unhoused, and 30.4% had skin wounds currently. Xylazine positivity in urine for the 2 strip types used was 82.6% and 65.2%. For paraphernalia testing, the xylazine positivity was 65.2% and 47.8%. Confirmatory testing of paraphernalia samples by mass spectrometry indicated a 52.2% xylazine positivity, as well as fentanyl (73.9%), fluorofentanyl (30.4%), tramadol (30.4%), and lidocaine (30.4%). Mass spectrometry suggested lidocaine triggered n=3 and n=0 false positives among the xylazine test strip types. DISCUSSION: Xylazine is present on the US-Mexico border, requiring public health intervention. High lidocaine positivity complicates the clinical detection of xylazine via testing strips. Routine urine testing for xylazine in clinical scenarios is likely feasible, yet confirmatory urine studies are needed.
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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 | 0.004 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".