Recurrent seizures in cats: Treatment – which antiepileptic drugs are recommended?
Bibliographic record
Abstract
Practical relevance: Seizures are one of the most common neurological problems recognized in cats, affecting approximately 1-3% of the general population. Treatment options and prognosis are closely related to the underlying cause, so it is important that veterinarians are familiar with the diagnostic approach to cats with seizures and options for medical management. Series outline: This is the second of a two-part article series that reviews the diagnosis and treatment of seizures in cats. Part 2 describes chronic medical treatment options and prognosis for cats with recurrent seizures, and acute treatment of status epilepticus. AUDIENCE: This review of recurrent seizures in cats is intended for all veterinarians who are facing the challenges of seizure diagnosis and management in the feline patient. Evidence base: Recommendations for diagnosis and management of feline seizure disorders have historically been extrapolated from the canine and human literature. The information and guidance provided in this two-part series is based on a review of the recent published literature addressing seizure disorders and antiepileptic treatment in cats, as well as the authors' clinical experience.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".