Tools and techniques for classifying behaviours in canine epilepsy
Bibliographic record
Abstract
Idiopathic epilepsy is the most common neurological disease in dogs. Similar to humans, dogs with epilepsy often experience behavioural comorbidities such as increased fear, anxiety, and aggression, as reported by their caregivers. Investigations of behaviour in canine epilepsy have yet to untangle interictal and pre and postictal behaviours, prodromal changes, and seizure-precipitating factors. Under-recognition of absence and focal seizures further complicates these assessments. These complex behavioural presentations in combination with caring for an epileptic animal have a significant negative impact on the dog's and caregiver's quality of life. Despite the growing recognition of behavioural comorbidities and their impact on quality of life in dogs with epilepsy, few objective research methods for classifying and quantifying canine behaviour exist. This narrative review examines the strengths, limitations, and granularity of three tools used in the investigation of canine behaviour and epilepsy; questionnaires, electroencephalography, and actigraphy. It suggests that a prospective combination of these three tools has the potential to offer improvements to the objective classification and quantification of canine behaviour in epilepsy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".