Randomized Trials and Collaborative Research in Epilepsy Surgery: Future Directions
Bibliographic record
Abstract
BACKGROUND: Although randomized controlled trials (RCTs) are the gold standard for evaluating therapeutic interventions, surgical RCTs are particularly challenging and few have been done in the field of epilepsy surgery. We assess the level of RCT activity in epilepsy surgery and propose feasible alternatives to develop sustainable research initiatives in this area. METHODS: We undertook a systematic review of the world literature to assess the level of RCT activity in epilepsy surgery. Previous personal experience with RCTs in epilepsy surgery and examples of successful Canadian multicentre research networks were reviewed to propose initiatives for sustainable, valid research in epilepsy surgery. RESULTS: We identified 12 RCTs in epilepsy surgery, including 692 patients, of whom 416 were involved in vagus nerve stimulation, 16 in various brain electrostimulation procedures, 180 in comparisons of different surgical techniques, and 80 in a comparison of medical versus surgical therapy. Most studies were of short duration (median=3 months, range 3-12 months). In the area of resective surgery, only temporal lobe epilepsy has been subjected to any type of RCT comparison. All RCTs have been done within the last 13 years. There were no multicentre Canadian surgical studies. CONCLUSIONS: The adoption of RCTs in epilepsy surgery has been slow and difficult worldwide. Because of its universal health care system and its well established epilepsy surgery centres, Canada is in a strong position to create a national epilepsy surgery research initiative capable of undertaking high quality, sustainable research in epilepsy surgery.
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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 | 0.463 | 0.531 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.016 | 0.035 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.031 | 0.018 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".