Seizure frequency discrepancy between subjective and objective ictal electroencephalography data in dogs
Bibliographic record
Abstract
BACKGROUND: Many studies of epilepsy in veterinary medicine use subjective data (eg, caregiver-derived histories) to determine seizure frequency. Conversely, in people, objective data from electroencephalography (EEG) are mainly used to diagnose epilepsy, measure seizure frequency and evaluate efficacy of antiseizure drugs. These EEG data minimize the possibility of the underreporting of seizures, a known phenomenon in human epileptology. OBJECTIVE: To evaluate the correlation between reported seizure frequency and EEG frequency of ictal paroxysmal discharges (PDs) and to determine whether seizure underreporting phenomenon exists in veterinary epileptology. ANIMALS: Thirty-three ambulatory video-EEG recordings in dogs showing ≥1 ictal PD, excluding dogs with status epilepticus. METHODS: Retrospective observational study. Ictal PDs were counted manually over the entire recording to obtain the frequency of EEG seizures. Caregiver-reported seizure frequency from the medical record was categorized into weekly, daily, hourly, and per minute seizure groupings. The Spearman rank test was used for correlation analysis. RESULTS: values comparing history against various seizure types were: 0.36 for motor seizures and 0.37 for nonmotor (absence) seizures. CONCLUSIONS AND CLINICAL IMPORTANCE: A weak correlation was found between the frequency of reported seizures from caregivers (subjective data) and ictal PDs on EEG (objective data). Subjective data may not be reliable enough to determine true seizure frequency given the discrepancy with EEG-confirmed seizure frequency. Confirmation of the seizure underreporting phenomenon in dogs by prospective study should be carried out.
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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 | 0.002 | 0.007 |
| 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 | 0.000 | 0.000 |
| 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".