Subtypes of Post-Traumatic Epilepsy: Clinical, Electrophysiological, and Imaging Features
Bibliographic record
Abstract
Post-traumatic epilepsy (PTE) is a consequence of traumatic brain injury (TBI), occurring in 10-25% of patients with moderate to severe injuries. The development of animal models for testing antiepileptogenic therapies and validation of biomarkers to follow epileptogenesis in humans necessitates sophisticated understanding of the subtypes of PTE, which is the objective of this study. In this study, retrospective review was performed of patients with moderate to severe TBI with subsequent development of medically refractory epilepsy referred for video-electroencephalography (EEG) monitoring at a single center over a 10-year period. Information regarding details of injury, neuroimaging studies, seizures, video-EEG, and surgery outcomes were collected and analyzed. There were 123 patients with PTE identified, representing 4.3% of all patients evaluated in the epilepsy monitoring unit. Most of them had localization-related epilepsy, of which 57% had temporal lobe epilepsy (TLE), 35% had frontal lobe epilepsy (FLE), and 3% each had parietal and occipital lobe epilepsy. Of patients with TLE, 44% had mesial temporal sclerosis (MTS), 26% had temporal neocortical lesions, and 30% were nonlesional. There was no difference in age at injury between the different PTE subtypes. Twenty-two patients, 13 of whom had MTS, proceeded to surgical resection. At a mean follow-up of 2.5 years, Engel Class I outcomes were seen in 69% of those with TLE and 33% of those with FLE. Our findings suggest PTE is a heterogeneous condition, and careful evaluation with video-EEG monitoring and high resolution MRI can identify distinct syndromes. These results have implications for the design of clinical trials of antiepileptogenic therapies for PTE.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".