Pharmaceutical exposure and toxicosis in dogs: A retrospective study of 223 cases from a Canadian veterinary teaching hospital (2018 to 2023).
Bibliographic record
Abstract
Background: Ingestion of pharmaceuticals is a common cause of poisoning and hospitalization in companion animals. Pets may be exposed through accidental over-administration of a prescribed veterinary drug, intentional administration of a human drug that owners do not realize is unsuitable for animals, or access to unattended medications. Objective: Our objective was to document cases of exposure and toxicosis due to suspected and confirmed pharmaceutical ingestion in dogs admitted to a veterinary teaching hospital over a 6-year period (2018 to 2023). Animals and procedure: Medical records were retrieved from the veterinary hospital database using keywords related to general poisoning. Results were then filtered using keywords related specifically to pharmaceutical ingestion while excluding non-pharmaceutical poisoning cases. Information pertaining to hospitalization, patient signalment, treatment, and case progression was collected and analyzed to characterize common factors in canine pharmaceutical poisoning cases. Results: = 12, respectively). The occurrence of cases related to exposure to human pharmaceuticals was 5× that of cases related to veterinary pharmaceuticals. Only 1 dog of 223 was euthanized, for a survival-to-discharge rate of 99.6%. The most common therapies administered were emesis induction, activated charcoal, fluid support, and gastroprotectant. Conclusion and clinical relevance: Pharmaceutical exposure, especially from over-the-counter human medications, was a common reason for hospital admission among the dogs described in this study. Improved client education is needed to avoid preventable pharmaceutical exposures.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".