Risk factors for cluster seizures in canine idiopathic epilepsy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cluster seizures (CS), two or more seizures within a 24-hour period, are reported in 38-77% of dogs with idiopathic epilepsy (IE). Negative outcomes associated with CS include a reduced likelihood of achieving seizure freedom, decreased survival time and increased likelihood of euthanasia. Previous studies have found factors including breed, sex and neuter status are associated with CS in dogs with IE; however, only one UK study in a multi-breed study of CS in IE patients exists to the author's knowledge, and thus further data is required to confirm these results. Data from 384 dogs treated at a multi-breed canine specific epilepsy clinic were retrospectively collected from electronic patient records. 384 dogs were included in the study, of which nearly half had a history of CS (49.1%). Dogs with a history of CS had a younger age at onset than those without (p = 0.033). In a multivariate model, three variables predicted risk of CS: a history of status epilepticus (p = 0.047), age at seizure onset (p = 0.066) and breed (German Shepherd Dog) (p < 0.001). Dogs with a history of status epilepticus and dogs with an older age at seizure onset were less likely to be affected by cluster seizures. German Shepherd Dogs (71% experiencing CS) were significantly more likely to suffer from CS compared to Labrador Retrievers (25%) (p < 0.001). There was no association between sex, neuter status, body size and CS. Further studies into the pathophysiology and genetics of CS are required to further understand this phenomenon.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it