Bibliographic record
Abstract
PURPOSE OF REVIEW: Using the most recent evidence, we provide an update about epilepsy surgery, focusing on the presurgical evaluation and surgical planning, epilepsy surgery outcomes, and utilization. RECENT FINDINGS: Great strides are being achieved in the presurgical evaluation and planning for epilepsy surgery, including fundamental advances in imaging and neurophysiology. A recent randomized controlled trial demonstrates that early surgery for patients with mesial temporal lobe epilepsy (TLE) is superior to medical therapy. The enduring benefits of surgery continue to be demonstrated, particularly after TLE surgery. However, studies examining the long-term outcomes after extratemporal lobe epilepsy surgery are scarce. Surgery is generally associated with an improvement in depression, but mostly in those with good surgical outcome. Complications from invasive monitoring or after epilepsy surgery are generally temporary, or limited in their symptomatology. One area in need of prospective studies is the topic of antiepileptic drug withdrawal after epilepsy surgery (Who? When? How?). Despite its proven effectiveness, epilepsy surgery continues to be underutilized, but new tools for health professionals are emerging to guide appropriate surgical referrals. SUMMARY: Important contributions to the field of epilepsy surgery are discussed, in particular emerging imaging (fMRI) and neurophysiological (high-frequency oscillations) techniques. Epilepsy surgery is effective, well tolerated but still underutilized.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".