Bibliographic record
Abstract
PURPOSE OF REVIEW: Using the most recent evidence, we provide an update on epilepsy surgery, focusing on its effectiveness, reasons for underutilization, considerations of candidacy and timing for referral for epilepsy surgery evaluation. RECENT FINDINGS: The course of illness of epilepsy is being characterized. Well conducted studies describe the patterns of seizure remission and relapse with medical therapy and also in response to epilepsy surgery. Epilepsy surgery is highly effective in selected patients with drug-resistant epilepsy (DRE). The risk-benefit of epilepsy surgery is well known and consistent around the world. However, epilepsy surgery remains underutilized. A randomized controlled trial and Clinical Practice Guidelines (CPGs) supporting epilepsy surgery have had no discernible impact on referral rates for epilepsy surgery evaluation. Criteria and guidelines are being developed for identifying patients who need to be referred for epilepsy surgery evaluation. Quality indicators for epilepsy care now also include the need to consider surgical candidacy every 3 years in DRE. New developments in imaging and neurophysiology promise to help clinicians identify and treat patients more accurately. SUMMARY: Surgery is effective but underused. Comprehensive interventions to translate evidence to practice in epilepsy surgery are urgently needed.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".