Discontinuation of antiepileptic drugs after successful surgery: who and when?
Bibliographic record
Abstract
Surgery is a highly effective treatment for some specific types of refractory epilepsy and once seizure freedom is achieved many patients and clinicians have to ponder whether to taper or discontinue antiepileptic drugs (AEDs). However, there is no standard practice or guidelines and practices vary widely. The few studies that have addressed this question are retrospective and lack randomised, controlled comparisons, making it difficult to draw any solid inferences. This review examines this topic by analysing key data based on the following: controlled studies which compare outcomes in patients with either withdrawn or unmodified AEDs after epilepsy surgery, non-controlled studies, information from meta-analyses and systematic reviews, surveys of clinical practice, and other relevant reviews. Between 12 and 32% of patients had seizure relapse following tapering or discontinuation of AEDs, which was not significantly different from 7 to 45% in patients without AED modification. In the event of seizure relapse upon tapering of AEDs, 45-92.3% restarted AED treatment and regained seizure freedom. The most consistent risk factors for seizure relapse were: age older than 30 years at the time of surgery, persistent auras, early drug tapering, seizure recurrence before a reduction of drugs, normal MRI, a longer period with epilepsy, absence of hippocampal sclerosis, and the presence of interictal discharges on EEG after surgery.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".