Prevalence of generalized and non-generalized seizure types in a primary care population of dogs
Bibliographic record
Abstract
Objective: To estimate the prevalence of generalized tonic-clonic seizures (GTCS), generalized tonic-clonic seizures plus additional signs (GTCS+), and non-generalized tonic-clonic seizures (non-GTCS) in a single primary care practice canine population and to explore whether insurance and preventative healthcare plan enrollment affect referral care acceptance. Methods: This was a retrospective descriptive study of electronic medical records of dogs with at least 1 seizure recorded (even if comorbid) attending a single primary care veterinary clinic from May 2017 to April 2023. Seizure descriptions (categorized as above), healthcare plans, and demographic variables were collected. Results: Of 28 cases, 16 (57%) were GTCS, 9 (32%) were GTCS+, and 3 (11%) were non-GTCS. Only 3 (11%) had a record of insurance, while 20 (71%) had preventative healthcare plan enrollment. Of note, 16 of 28 (57%) cases recorded neurology referral care, with the majority of cases being uninsured (13 of 16 [81%]). Conclusions: Of the few cases in the 6-year period, the most described were GTCS (57%), followed by GTCS+ (32%) and non-GTCS (10%). Larger primary care populations are needed for robust estimates of seizure and demographic prevalence and to examine financial barriers to care. Clinical Relevance: Investigations at the level of a single primary care practice capture the context and detail of seizure presentations. Discrepancies exist between the spectrum of seizure type diagnosis and care provided within primary care and referral establishments. Integration of International Veterinary Epilepsy Task Force (IVETF) protocols and advanced diagnostic procedures into veterinary education could facilitate access to a wider spectrum of canine seizure care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".