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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".