LEVETIRACETAM-ASSOCIATED PSYCHOGENIC NON-EPILEPTIC SEIZURES: A HIDDEN PARADOX
Bibliographic record
Abstract
Objectives To study the clinical profile and outcome in patients with epilepsy who developed psychogenic non-epileptic seizures (PNES) associated with levetiracetam (LEV) use. Methods In this prospective observational study, conducted over 1 year, 13 patients with epilepsy and PNES, documented by video electroencephalogram (VEEG) while on LEV, were included. Those with past history of psychiatric illnesses were excluded. VEEG, high-resolution magnetic resonance imaging, neuropsychological and psychiatric evaluation were performed. Patients in Group I (07) were treated with psychotherapy, psychiatric medications and immediate withdrawal of LEV while, those in Group II (06) received psychotherapy, anxiolytics and LEV for initial 2 months after which it was stopped. Follow-up period was six months. There was subsidence of PNES on discontinuation of LEV in these patients. Results Mean (±SD) age of patients was 25 ± 12.28 years; there were 11 (84.62%) females. All were on antiepileptic agents which included LEV >1000 mg/day, except one. Mean dose of LEV was 1269.23 ± 483.71 mg/day. Three patient's scores were suggestive of depression or anxiety; one had both depression and anxiety. Eight patients had mood disorders; three had a history of emotional abuse or neglect. PNES subsided within 1-3 months and did not recur after withdrawal of LEV in any patient. Conclusion LEV can induce PNES in susceptible populations. Awareness of this association is crucial for timely withdrawal of triggering factor and appropriate management. This will reduce inadvertent additional prescription of antiepileptic agents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".