Management of Epileptic Seizures in Disorders of Consciousness: An International Survey
Bibliographic record
Abstract
Epileptic seizures/post-traumatic epilepsy (ES/PTE) are frequent in persons with brain injuries, particularly for patients with more severe injuries including ones that result in disorders of consciousness (DoC). Surprisingly, there are currently no best practice guidelines for assessment or management of ES in persons with DoC. This study aimed to identify clinician attitudes toward epilepsy prophylaxis, diagnosis and treatment in patients with DoC as well as current practice in regards to the use of amantadine in these individuals. A cross-sectional online survey was sent to members of the International Brain Injury Association (IBIA). Fifty physician responses were included in the final analysis. Withdrawal of antiepileptic drug/anti-seizure medications (AED/ASM) therapy was guided by the absence of evidence of clinical seizure whether or not the AED/ASM was given prophylactically or for actual seizure/epilepsy treatment. Standard EEG was the most frequent diagnostic method utilized. The majority of respondents ordered an EEG if there were concerns regarding lack of neurological progress. AED/ASM prescription was reported to be triggered by the first clinically evident seizure with levetiracetam being the AED/ASM of choice. Amantadine was frequently prescribed although less so in patients with epilepsy and/or EEG based epileptic abnormalities. A minority of respondents reported an association between amantadine and seizure. Longitudinal studies on epilepsy management, epilepsy impact on neurologic prognosis, as well as potential drug effects on seizure risk in persons with DoC appear warranted with the goal of pushing guideline development forward and improving clinical assessment and management of seizures in this unique, albeit challenging, population.
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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 | 0.003 |
| 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 | 0.000 |
| 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".