Epidemiology and risk factors of seizures in dogs: Insights into breed, age, gender and management practices
Bibliographic record
Abstract
Seizures are a common neurological emergency in dogs, arising from diverse etiologies including idiopathic, structural, metabolic, infectious and toxic causes. This study evaluated 126 dogs with seizures to investigate breed predisposition, age of onset, gender, neutering status, preventive care, source of adoption and dietary factors. Non-descript dogs (20.6%) were most frequently affected, followed by Golden Retrievers (12.7%) and Labrador Retrievers (11.1%). Seizures were most commonly observed between 6 months and 6 years of age (57.9%), predominantly idiopathic/structural in origin, while organ dysfunction and infectious causes were more common in geriatric and juvenile dogs, respectively. A higher proportion of affected dogs were male (60.3%) and intact (64.3%). Preventive care was suboptimal, with irregular or absent vaccination and deworming noted in a majority of dogs. Dogs acquired from breeders/kennels (34.1%) exhibited higher seizure incidence, likely reflecting genetic predispositions and dietary patterns indicated increased risk in dogs fed non-vegetarian diets (46.0%). These findings underscore the multifactorial nature of seizures in dogs, highlighting the influence of breed, age, sex, preventive care, adoption source and diet.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".