Levetiracetam in Clinical Practice: Efficacy and Tolerability in Epilepsy
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to evaluate efficacy and tolerability of levetiracetam (LEV) in patients with different epilepsy syndromes. METHODS: We evaluated epileptic patients seen in the previous 18 months, including all patients with present or past exposure to LEV. Tolerability of LEV therapy was evaluated in all patients; efficacy was evaluated only in patients who had received LEV for at least six months. Two hundred and two patients were included in the study. Patients were considered responsive when showing a > 50% reduction in seizures frequency and non-responders when seizure frequency was unchanged, worsened or showed a reduction < 50%. RESULTS: Thirty patients did not complete six months of LEV treatment and dropped out. 57.4% of the patients with uncontrolled seizures treated for at least six months were responders, with 27.7% seizure free. Adverse effects were observed in 46 patients (23%) and were responsible for early drop out in 26. Adverse effects occurred significantly more often in females than in males (30.6% vs 13.2%); moreover, nearly 30% of women with adverse effects complained of more than one adverse effect, while this was never observed in male patients. CONCLUSIONS: Our study shows LEV as a well tolerated and effective treatment, both in monotherapy and as an add-on. Further investigations on larges samples are needed to investigate the issue of gender-related tolerability.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".