Analysis of cannabinoids in plasma from 38 cases of suspected cannabinoid intoxication in dogs
Bibliographic record
Abstract
OBJECTIVE: To quantify and characterize plasma cannabinoid concentrations in cases of suspected cannabis toxicity in dogs, identify potential correlations between clinical signs and plasma concentrations, and assess the specificity of cannabis toxicity diagnosis based on clinical signs alone. DESIGN: Observational study. SETTING: Veterinary teaching hospital. ANIMALS: Thirty-eight client-owned animals. INTERVENTIONS: -tetrahydrocannabinol (THC), cannabidiol (CBD), and their active metabolites. MEASUREMENTS AND MAIN RESULTS: The most common abnormality observed was ataxia (35/38 dogs), with urinary incontinence, lethargy, and hyperesthesia also commonly noted. Cannabinoids were quantifiable in 37 of 38 plasma samples (97.4%), with THC the predominant cannabinoid (range: 1.99-2748 ng/mL). Lower concentrations of CBD (up to 115.3 ng/mL) and cannabinoid metabolites were detected. Of the clinical signs recorded, only abnormal reflexes were statistically correlated with the THC concentration at the time of sampling (P = 0.01). CONCLUSIONS: A diagnosis of suspected cannabinoid toxicity based on case history and clinical presentation was confirmed via quantifiable plasma concentrations in nearly all cases. Although the range of plasma cannabinoid concentrations was broad, the clinical signs observed were generally similar. Other than the presence of abnormal reflexes, clinical signs were not associated with plasma THC concentrations. Subsequent confirmation of cannabinoids in plasma indicates that cannabis toxicity in dogs can be diagnosed with high specificity by veterinarians based only on history and clinical abnormalities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".