Brivaracetam in the Treatment of Patients with Epilepsy—First Clinical Experiences
Bibliographic record
Abstract
OBJECTIVES: To assess first clinical experiences with brivaracetam (BRV) in the treatment of epilepsies. METHODS: Data on patients treated with BRV from February to December 2016 and with at least one clinical follow-up were collected from electronic patient records. Data on safety and efficacy were evaluated retrospectively. RESULTS: = 20) in this group, of which five (8.8%) patients were newly seizure-free. In 50.5% (47/93), patients were switched from levetiracetam (LEV) to BRV, of which 43 (46.2%) were switched immediately. Adverse events (AE) occurred in 39.8%, with 22.6% experiencing behavioral and 25.8% experiencing non-behavioral AE. LEV-related AE (LEV-AE) were significantly reduced by switching to BRV. The discontinuation of BRV was reported in 26/93 patients (28%); 10 of those were switched back to LEV with an observed reduction of AE in 70%. For clinical reasons, 12 patients received BRV in monotherapy, 75% were seizure-free, and previous LEV-AE improved in 6/9 patients. BRV-related AE occurred in 5/12 cases, and five patients discontinued BRV. CONCLUSION: BRV seems to be a safe, easy, and effective option in the treatment of patients with epilepsy, especially in the treatment of patients who have psychiatric comorbidities and might not be good candidates for LEV treatment. BRV broadens the therapeutic spectrum and facilitates personalized treatment.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".