EEG: Characteristics of drug-induced seizures in rats, dogs and non-human primates
Bibliographic record
Abstract
Seizures are amongst the most frequent neurological issues encountered in pre-clinical safety testing. The objective was to characterize EEG morphologies and premonitory signs in drug-induced seizures in preclinical species. A comparative (inter-species) retrospective analysis for drug-induced seizures recorded by video-telemetry was conducted in rats (n = 53), dogs (n = 195), and non-human primates (n = 234). The most frequent premonitory signs were, in rats, myoclonus (100%), tremors (93%), salivation (75%), partial ptosis (58%) and chewing/bruxism (58%); in dogs, tremors (77%), ataxia/uncoordination (60%), myoclonus (45%), salivation (43%), excessive licking (38%), high vocalization (38%) and decreased activity (34%); in non-human primates, tremors (79%), decreased activity (70%), myoclonus (57%), retching/emesis (37%), hunched posture (30%) and ataxia/uncoordination (27%). Seizure duration ranged from 3 s to 14 min with an average of 46 ± 21 s, comparable across species. At seizure onset, spike frequency averaged 9.4 Hz for the three species compared to 4.3 Hz at seizure end. Peak average amplitudes were attained at mid-seizure and amplitudes at seizure end decreased from peak but remained higher than onset amplitudes. Spike duration was inversely correlated with frequency and presented a crescendo pattern. Morphological characteristics can serve to refine automated EEG analysis. From a regulatory perspective, the most common paradigm is to use the most sensitive species in seizure liability studies but translational potential and clinical relevance may be under represented in the decision making process in some cases. EEG morphologies during drug-induced seizures presented remarkable similarities between species and tremors were identified as a predominant premonitory clinical sign in all species.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".