Effective withdrawal of antiepileptic drugs in premonitoring admission to capture seizures during limited video‐<scp>EEG</scp> monitoring
Bibliographic record
Abstract
Objective: Withdrawal of antiepileptic drugs (AEDs) is commonly applied to capture seizures in video-EEG (vEEG) monitoring for patients with infrequent but intractable seizures. Because of the half-life of AEDs, AED withdrawal during only vEEG tends to be inadequate to provoke seizures within the vEEG admission. We hypothesize that prewithdrawal of long-half-life AEDs in premonitoring admission (PMA) is safe and effective to capture seizures in the limited time of vEEG. We determined the effect of half-life on the interval between AED withdrawal and seizure occurrence. Methods: We collected 87 patients with three criteria: (1) seizure occurrence ≤3 per month; (2) AEDs ≥2; (3) AED withdrawal during their admission, among 126 consecutive patients who underwent vEEG in the Department of Neurosurgery, Hiroshima University Hospital between 2011 and 2014. We divided patients into two groups on the basis of half-life of AED: Group A (23 patients) with phenobarbital (PB) and/or zonisamide (ZNS); Group B (64 patients) with other AEDs. In Group A, PB and ZNS were withdrawn during 4-day PMA before vEEG started. Further AED withdrawal was performed during vEEG, depending on the seizure occurrence. Results: The number of AEDs on admission was significantly higher in Group A (2-6, 3.5 ± 0.9; range, mean ±SD) than in Group B (2-5, 2.8 ± 0.8) (p < 0.01). All 23 Group A patients and 13 (20%) Group B patients underwent AED withdrawal during PMA. Seizures occurred during PMA in two patients in both Group A (9%) and Group B (15%). The first seizure occurred significantly longer after the start of withdrawal in Group A (6.1 ± 2.0 days) than in Group B (2.8 ± 1.3 days) (p < 0.01). Seizures were equally captured between both groups: 96% in Group A and 92% in Group B during vEEG. Significance: For epilepsy patients who are treated with PB and/or ZNS, we recommend the planning of AED withdrawal during PMA before the start of vEEG to succeed in capturing seizures during the limited time of vEEG monitoring.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".