Seizure occurrence in dogs under primary veterinary care in the UK: prevalence and risk factors
Bibliographic record
Abstract
BACKGROUND: Primary-care veterinary clinical records can offer data to determine generalizable epidemiological data on seizures occurrence in the dog population. OBJECTIVES: To identify and examine epidemiologic characteristics of seizure occurrence in dogs under primary veterinary care in the UK participating in the VetCompass™ Programme. ANIMALS: 455,553 dogs in VetCompass™'. METHODS: A cross-sectional analysis estimated the 1-year period prevalence and risk factors for dogs with seizures during 2013. RESULTS: The overall 1-year period prevalence for dogs having at least one seizure during 2013 was 0.82% (95% CI 0.79-0.84). Multivariable modelling identified breeds with elevated odd ratios [OR] compared with the Labrador Retriever (e.g. Pug OR: 3.41 95% CI 2.71-4.28, P < 0.001). Males had higher risk for seizures (Male/Entire OR: 1.47 95% CI 1.30-1.66; Male/Neutered OR: 1.34 95% CI 1.19-1.51) compared to entire females. Age (3.00 - ≤ 6.00 OR: 2.13 95% CI 1.90-2.39, P < 0.001, compared to animals aged 0.50-≤ 3.00 years), and bodyweight (≥ 40.00kg, OR: 1.24 95% CI 1.08-1.41, P = 0.002, compared to animals weighing < 10.0 kg) were identified as risk factors for seizures. CONCLUSION AND CLINICAL IMPORTANCE: Seizures are a relatively common clinical finding in dogs. The results for breed, age, sex and bodyweight as risk factors can assist veterinarians in refining differential diagnosis lists for dogs reported with behaviors that may have been seizures. In addition, the prevalence values reported here can support pharmacovigilance with baseline data from the overall population.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".