Frequency of non-generalized tonic clonic seizures in a referral population of dogs
Bibliographic record
Abstract
Absence seizures are a type of generalized onset seizure associated in humans with brief activity interruptions, unresponsiveness and staring. Absence seizures are infrequently reported in veterinary patients, visually indistinguishable from focal seizures, and so may be grouped as non-generalized tonic clonic seizures (non-GTCS). The objective of this retrospective study was to provide a preliminary understanding of the frequency of non-GTCS in dogs and estimate its prevalence by evaluating the distribution of seizure types presented to a referral hospital over 4 years (May 2017-April 2021), as determined from the medical record history and electroencephalography (EEG) diagnostic testing where available. A total of 528 cases were included via a medical record search for dogs with epilepsy and/or seizures presented to the neurology or emergency services. Cases were categorized into seizure types based on reported clinical signs. Each year, 53-63 % of seizure cases were described as generalized tonic clonic seizures (GTCS), 9-15 % GTCS with additional events and 29-35 % suspected non-GTCS. EEG confirmed absence seizures in 12 of 44 EEGs, 5 cases having a history of GTCS and seven without prior GTCS. This preliminary study suggests that non-GTCS may be relatively common as one third of seizure cases in the referral population presented with non-GTCS clinical signs. Prospective studies using EEG are merited to definitively determine the prevalence of these different seizure types in dogs. Acknowledging the impact of these seizures will improve awareness, aiding veterinarians in their recognition, diagnosis and potential treatment options.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".