Seizure-precipitating factors in dogs with idiopathic epilepsy
Bibliographic record
Abstract
BACKGROUND: Stress, sleep deprivation, and infectious diseases are important seizure-precipitating factors in human epilepsy patients. However, these factors have not been thoroughly studied in epileptic dogs. OBJECTIVE: Seizure-precipitating factors are common in dogs with idiopathic epilepsy and the occurrence of these factors associate with the dogs' signalment, personality, and epilepsy-related factors. ANIMALS: Fifty dogs with diagnosed idiopathic epilepsy from the hospital populations of University Veterinary Teaching Hospital of University of Helsinki and Referral Animal Hospital Aisti. METHODS: In a retrospective cross-sectional observational study, owners were interviewed about their dogs' possible seizure-precipitating factors according to a predefined questionnaire. The dogs were identified and selected by searching the medical records of the participating animal hospitals. RESULTS: The prevalence of seizure-precipitating factors in the study population was 74% (37/50). The most frequently reported factors included stress-related situations, sleep deprivation, weather, and hormonal factors. In dogs with focal onset seizures, the number of precipitating factors was 1.9 (95% CI 1.1-3.4) times higher compared to dogs with generalized seizures. CONCLUSIONS AND CLINICAL IMPORTANCE: Seizure-precipitating factors are common in dogs with idiopathic epilepsy, and the nature of these factors is consistent with those of human patients. Aside from antiepileptic medication, acknowledging and avoiding seizure-precipitating factors could help veterinarians achieve better treatment outcomes.
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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 | 0.000 |
| 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".