Not all that glitters is gold: A guide to surgical trials in epilepsy
Bibliographic record
Abstract
Epilepsy surgery is often the only effective treatment in appropriately selected patients with drug-resistant epilepsy, a disease affecting about 30% of those with epilepsy. We review the evidence supporting the use of epilepsy surgery, with a focus on randomized controlled trials (RCTs). Second, we identify gaps in knowledge about the benefits of epilepsy surgery for certain populations, the challenges of individualizing the choice of surgery, and our lack of understanding of the mechanisms of surgical outcomes. We conducted a search (MEDLINE, Embase, Cochrane, Clinicaltrials.gov) on March 2, 2016, to identify epilepsy surgery RCTs, systematic reviews, or health technology assessments (HTAs). Abstracts were screened to identify resective, palliative (e.g., corpus callosotomy, multiple subpial transection [MST]), ablative (e.g., Laser interstitial thermal therapy [LITT], gamma knife radiosurgery [RS]), and neuromodulation (e.g., cerebellar stimulation [CS], hippocampal stimulation [HS], repetitive transcranial magnetic stimulation [rTMS], responsive neurostimulation [RNS], thalamic stimulation [TS], trigeminal nerve stimulation [TNS], and vagal nerve stimulation [VNS]) RCTs. Study characteristics and outcomes were extracted. Knowledge gaps were identified. Of 1,205 abstracts, 20 RCTs were identified (resective surgery including corpus callosotomy [n = 7], MST [n = 0], RS [n = 1, 3 papers], LITT [n = 0], CS [n = 1], HS [n = 2], RNS [n = 1], rTMS [n = 1], TNS [n = 1], TS [n = 1], and VNS [n = 5]). Most studies targeted patients with temporal lobe epilepsy (TLE) and none examined the effectiveness of resective surgical therapies in patients with extra-TLE (ETLE) or with specific lesions aside from mesial temporal lobe sclerosis. No pediatric surgical RCTs were identified except for VNS. Few RCTs address the effectiveness of surgery in epilepsy and most are of limited generalizability. Future studies are needed to compare the effectiveness of different surgical strategies, better understand the mechanisms of surgical outcomes, and define the ideal surgical approaches, particularly for patients with high or very low cognitive function, normal imaging, or ETLE.
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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 | 0.190 | 0.420 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".