Development of a Novel Epilepsy and Dyskinesia Survey for Large-Scale Characterization of Seizure Semiology in Dogs
Bibliographic record
Abstract
BACKGROUND: Diagnosing epilepsy and dyskinesia in dogs relies on seizure semiology, laboratory workup, brain imaging, and electroencephalography. Variability in existing epilepsy surveys complicates comparison and impedes epidemiologic and genetic research. OBJECTIVE: To characterize the semiology of epileptic seizures and dyskinesia episodes using a novel, owner-completed, multi-language online questionnaire. ANIMALS: A cohort of 606 dogs from 96 breeds with paroxysmal events, perceived by their owners as epilepsy or dyskinesia. MATERIALS AND METHODS: A comprehensive epilepsy and dyskinesia questionnaire featuring pragmatic seizure categories and video upload was developed in German, Finnish, and English. The reliability of the questionnaire was assessed, and the study cohort analyzed. RESULTS: The questionnaire demonstrated strong internal consistency and interrater agreement. Owners correctly classified paroxysmal events in 90.1% of cases (95% CI 88.18-92.11). Video footage was submitted from 23.8% (143/606) and supported the seizure type in the questionnaire in 96.5%. The age of onset ranged from 6 months to 6 years in 80.2% (median 2 years; IQR 1-5 years). Generalized (epileptic) convulsive seizures occurred in 58.6% of dogs, non-generalized paroxysmal motor events without convulsions in 58.1%, sudden falls without movement in 6.1%, episodes of impaired awareness in 15.8%, and other unclassified events in 7.1%. Multiple seizure types were reported in 25.2% of the dogs. Labrador Retrievers exhibited a higher prevalence of non-generalized motor events compared to Border Collies, Siberian Huskies, and other breeds (p < 0.001). CONCLUSIONS: The questionnaire reliably characterizes epileptic seizures and dyskinesia episodes in dogs, making it a valuable tool for large-scale epidemiological and genetic studies.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".