Epilepsy surgery, antiepileptic drug trials, and the role of evidence
Bibliographic record
Abstract
OBJECTIVE: We assessed whether recent randomized controlled trials (RCTs) of antiepileptic drugs (AEDs) are informed by evidence about surgical effectiveness. We explored whether RCTs of AEDs consider the patients' candidacy for surgery in their eligibility criteria, and whether the necessary investigations are requested in participating patients to determine their potential eligibility for surgery. METHODS: We systematically analyzed RCTs published in the last 2 years investigating the efficacy of new AEDs in localization-related epilepsy. Results from a surgical RCT and recommendations from an epilepsy surgery practice parameter were used to assess the degree to which surgical evidence informed the drug study design. RESULTS: Eleven RCTs were analyzed. All were conducted in countries with access to epilepsy surgery. None of the studies required magnetic resonance imaging (MRI) with an epilepsy protocol or explicit statement of the epilepsy syndrome, which could lead to the identification of surgical candidates. Having temporal lobe epilepsy or being a potential surgical candidate were not exclusion criteria in any of the trials. The primary efficacy end point was the reduction in seizure frequency or responder rate. Seizure freedom was never the primary outcome, and it was reported in only seven studies. The pooled data analysis of these trials revealed that 1.9% of patients became seizure-free on placebo and 4.4% on the study drug (p < 0.01). CONCLUSIONS: Important aspects of patient selection for new AED trials are not informed by the evidence about surgical effectiveness. Investigations that could lead to identification of patients for presurgical evaluation were not required in any of the studies.
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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 | 0.549 | 0.792 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".