Neurosurgery for the treatment of epilepsy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Epilepsy is a common condition that is estimated to afflict 0.5-1.0% of the world's population. Frequently commencing in childhood, it is often associated with life-long disability. Approximately one-third of patients with epilepsy are refractory to antiepileptic drug therapy and many of these patients are candidates for surgical treatment. A growing body of evidence supports the safety and efficacy of surgery for the treatment of selected patients with epilepsy. Little information is available in the anesthesia literature regarding the presurgical assessment of candidates for surgical treatment. RECENT FINDINGS: The presurgical identification of suitable candidates involves a multidisciplinary approach to assessment. Recent advances, particularly in neuroimaging techniques, are dramatically enhancing the capacity to accurately identify patients who are most likely to benefit from surgery. Epilepsy surgery is underused worldwide and in developed countries. In view of current efforts to increase opportunities to provide surgical treatment to more patients and to offer surgery earlier in the course of the disorder, the number of patients requiring specialized perioperative anesthetic care is expected to increase. SUMMARY: This article provides anesthesiologists with an overview of the assessment process, investigation techniques and current rationale that influence the selection of appropriate candidates for surgical treatment and the associated need for specialized anesthetic care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".