Exploring Human Misuse and Abuse of Veterinary Drugs: A Descriptive Pharmacovigilance Analysis Utilising the Food and Drug Administration’s Adverse Events Reporting System (FAERS)
Bibliographic record
Abstract
INTRODUCTION: Evidence suggests an increasing misuse of veterinary medicines by humans. This study aims to analyse Adverse Events (AEs) associated with selected veterinary products using the Food and Drug Administration Adverse Events Reporting System (FAERS). METHODS: A descriptive pharmacovigilance analysis was conducted on AEs related to 21 drugs approved for human and/or animal use. RESULTS: A total of 38,756 AEs, including 9566 fatalities, were identified. The United States reported the highest number of cases (13,532), followed by Canada (2869) and the United Kingdom (1400). Among the eight drugs licenced exclusively for animals, levamisole, pentobarbital, and xylazine were most frequently reported. Reports predominantly involved males (57%) from the 18-64 age group, with incidents related mainly to overdose, dependence, and multi-agent toxicities. Unmasking techniques revealed 'intentional overdose' as the primary reaction. Polysubstance use was evident in 90% of the drugs, with benzodiazepines/Z-drugs and opioids as common co-used classes. CONCLUSIONS: Veterinary medications are increasingly infiltrating the illicit drug market due to their pharmacological properties. This trend highlights the need for heightened vigilance and awareness to prevent further public health risks associated with the adulteration of illicit substances with veterinary products like xylazine and pentobarbital.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".