Bibliographic record
Abstract
PURPOSE: To review the following aspects of surgical randomized controlled trials (RCTs) in epilepsy: rationale, methodological issues, and state of the art. METHODS: An overview of the literature and author's experience with methodological issues in surgical RCTs, contrasting them with those encountered in medical trials; a general comparison of RCTs and non-RCTs (observational studies); and a literature search for and review of existing surgical RCTs in epilepsy. RESULTS: Nonrandomized (observational) studies provide useful but potentially biased information about the effect of interventions. Because of their ability to deal with bias, RCTs are the optimum vehicle to obtain a true estimate of the effect of interventions, including surgery. Only seven surgical RCTs, encompassing 535 patients, have been performed in epilepsy, with variable adhesion to standard methodology for conducting RCTs. The major issues facing researchers undertaking surgical RCTs in epilepsy include timing of the study in relation to adoption of the intervention as standard practice, acceptance of randomization to an invasive procedure, timing of randomization in relation to the actual intervention, standardization of the surgical procedure, blinding, and patient recruitment. CONCLUSIONS: RCTs are the gold standard for evaluating surgical treatments. Despite the large number and types of surgical interventions used to treat epilepsy, only a handful has been subjected to the scientific rigour of RCTs. The challenges faced by researchers undertaking surgical RCTs in epilepsy are substantial but not insurmountable. Possible avenues to address these methodological hurdles are suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".