Fentanyl, Amphetamine-type Stimulants, Heroin, and xylazine in drug residues in two Northern border Mexican cities
Bibliographic record
Abstract
Illicitly manufactured fentanyl (IMF) and amphetamine-type stimulants (ATS) are driving the fourth wave of the opioid crisis in the United States and Canada. Accordingly, the aim of this paper was to determine the presence of heroin, fentanyl, ATS, xylazine, and other substances in Tijuana and Mexicali, Baja California, two cities on the US-Mexico border. We also tested the positive predictive value of fentanyl and ATS test strips. To do this, we randomly selected 300 drug residues from syringe plungers provided by two non-governmental organizations (NGOs) with harm reduction programs (one in each city) and from community samples obtained at shooting galleries and meeting places for people who inject drugs. We then analyzed them with GC-MS and BTNX test strips for amphetamine-type stimulants (ATS) and fentanyl and its analogs. Test strips showed a high positive predictive value of 81% when compared with GC-MS. Most residues had fentanyl, followed by methamphetamine, heroin, and xylazine. Fentanyl and methamphetamine co-occurrence were the norm rather than the exception in drug residues. Xylazine was more prevalent in fentanyl-containing samples in Mexicali, whereas the combination of fentanyl and methamphetamine was more common in Tijuana. Benzodiazepines were not detected in any of our samples. In conclusion, in two key Mexican northern border cities, most heroin samples contained fentanyl. Opioid and ATS use was common in Tijuana and Mexicali, while xylazine emerged as a significant adulterant in Mexicali and was subsequently detected in Tijuana. The evolving drug market demands continuous drug testing while public health policies are required to address the challenges raised by this scenario.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".