Design considerations for a multicenter randomized controlled trial of early surgery for mesial temporal lobe epilepsy
Bibliographic record
Abstract
PURPOSE: To describe the trial design for the multicenter Early Randomized Surgical Epilepsy Trial (ERSET). Patients with pharmacoresistant epilepsy are generally referred for surgical treatment an average of two decades after onset of seizures, often too late to avoid irreversible disability. ERSET was designed to assess the safety and efficacy of early surgical intervention compared to continued pharmacotherapy. METHODS: ERSET is a randomized controlled, parallel group clinical trial with blinded outcome adjudication. Participants are patients with mesial temporal lobe epilepsy (MTLE) older than the age of 12 who have had pharmacoresistant seizures for not >2 years and are determined by detailed evaluation to be surgical candidates prior to randomization. The primary outcome measure is seizure freedom in the second year of a 2-year follow-up period. Health-related quality of life (HRQOL), neurocognitive function, ancillary outcomes, and adverse events were also measured. RESULTS: Significant methodologic problems addressed by the study design included the following: recruitment of participants early in the course of epilepsy; establishment of operational definitions for "pharmacoresistant" and "early"; and standardization of diagnostic testing, medical treatment, and surgical interventions across multiple centers. DISCUSSION: Rigorous trial designs to assess surgical interventions in epilepsy are necessary to provide evidence to guide treatment. This article is the first of a series; trial results will be reported in subsequent publications.
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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 | 0.240 | 0.225 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".