Illicit Fentanyl Exposures in Cats and Dogs Reported to a North American Veterinary Poison Control Center From 2019 to 2023
Bibliographic record
Abstract
OBJECTIVES: As a prominent concern for addiction medicine, illicit fentanyl and its analogues have been implicated in numerous poisonings among people. Many households in Canada and the United States include cats or dogs as pets who may be exposed to substances such as fentanyl. METHODS: This case-series examined data from the American Society for the Prevention of Cruelty to Animals' Poison Control, a 24-hour call center for animal poison-related emergencies. Descriptive statistics were used on records in which cats or dogs had a reported exposure to illicit fentanyl between 2019 and 2023. RESULTS: The sample included 117 animals (n=4 cats and n=113 dogs). Breeds most identified in this sample were Chihuahuas (n=21) and American Pit Bull Terriers (n=14). Among dogs, the average age was 1.9 (SD=2.8) years and the average weight was 10.5 (SD=11.1) kg. Among cats, the average age was 2.0 (SD=1.2) years and the average weight was 6.0 (SD=2.8) kg. Among dogs, the most reported illicit fentanyl exposure formulation included powder/crystals (n=34; 30.1%). Hypersalivation was reported as a clinical sign for n=2 (50.0%) cats in the sample. The top clinical signs reported among dogs in the sample include lethargy (n=39; 34.5%), vocalization (n=37; 32.7%), and ataxia (n=27; 23.9%). CONCLUSIONS: Impacts of illicit fentanyl and its analogues on society extend to 2 common household animals, cats and dogs. Any potential exposure to illicit fentanyl among cats or dogs should promptly seek emergency veterinarian services for reversal with naloxone and supportive care.
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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 | 0.002 | 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 | 0.000 |
| 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".