KOMET: an unblinded, randomised, two parallel-group, stratified trial comparing the effectiveness of levetiracetam with controlled-release carbamazepine and extended-release sodium valproate as monotherapy in patients with newly diagnosed epilepsy
Bibliographic record
Abstract
OBJECTIVE: To compare the effectiveness of levetiracetam (LEV) with extended-release sodium valproate (VPA-ER) and controlled-release carbamazepine (CBZ-CR) as monotherapy in patients with newly diagnosed epilepsy. METHODS: This unblinded, randomised, 52-week superiority trial (NCT00175903) recruited patients (≥16 years of age) with ≥2 unprovoked seizures in the previous 2 years and ≥1 in the previous 6 months. The physician chose VPA or CBZ as preferred standard treatment; each patient was randomised to standard treatment or LEV. The primary outcome was time to treatment withdrawal (LEV vs standard antiepileptic drugs (AEDs)). Analyses also compared LEV with VPA-ER, and LEV with CBZ-CR. FINDINGS: 1688 patients (mean age 41 years; 44% female) were randomised to LEV (n=841) or standard AEDs (n=847). Time to treatment withdrawal was not significantly different between LEV and standard AEDs: HR (95% CI) 0.90 (0.74 to 1.08). Time to treatment withdrawal (HR (95% CI)) was 1.02 (0.74 to 1.41) for LEV/VPA-ER and 0.84 (0.66 to 1.07) for LEV/CBZ-CR. Time to first seizure (HR, 95% CI) was significantly longer for standard AEDs, 1.20 (1.03 to 1.39), being 1.19 (0.93 to 1.54) for LEV/VPA-ER and 1.20 (0.99 to 1.46) for LEV/CBZ-CR. Estimated 12-month seizure freedom rates from randomisation: 58.7% LEV versus 64.5% VPA-ER; 50.5% LEV versus 56.7% CBZ-CR. Similar proportions of patients within each stratum reported at least one adverse event: 66.1% LEV versus 62.0% VPA-ER; 73.4% LEV versus 72.5% CBZ-CR. CONCLUSIONS: LEV monotherapy was not superior to standard AEDs for the global outcome, namely time to treatment withdrawal, in patients with newly diagnosed focal or generalised seizures.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".