Levetiracetam: an innovative and cost-effective add-on drug for refractory partial epilepsy
Bibliographic record
Abstract
Poorly-controlled epilepsy can have a significant negative impact on quality of life, clinically important changes are seen in patients emotional well-being, cognitive functioning, social functioning and energy/fatigue levels. Poorly-controlled epilepsy places an undue economic burden on the patient and community. Increased costs are seen in both direct and indirect healthcare costs (e.g., inpatient care and loss of earnings associated with time lost from work). Therefore, long-term efficacy and tolerability are key considerations when designing the patient's treatment regimen. Therapy can be individualized using both classical drugs and newer antiepileptics such as levetiracetam (Keppra, UCB Pharma Inc.), which is currently recommended as add-on therapy for partial-onset seizures. Studies have revealed characteristics that suggest levetiracetam is the first of a new class of antiepileptic drugs, differentiated by its innovative mechanism of action. Its efficacy and tolerability have enabled many patients who were refractory to treatment with other antiepileptic drugs to achieve long-term seizure freedom. Levetiracetam has a high long-term retention rate, a powerful measure of adverse events and efficacy over time. Another equally important benefit is ease of use, levetiracetam is administered twice-daily and has a simple titration regimen. Pharmacoeconomic data show that the incremental cost of treating patients with levetiracetam is low when compared with the benefits of seizure freedom. Ongoing studies suggest that this antiepileptic drug has potential as first-line treatment for many types of epilepsy and in many different patient populations.
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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 | 0.000 |
| 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) | 0.002 | 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".