Introduction. Surgical treatment of epilepsy
Bibliographic record
Abstract
E pilEpsy surgery is an exciting and dynamic field within neurosurgery.Advances in neuroimaging, neuroscience, neuromodulation, laser technology, robotics, and invasive neuromonitoring are being combined to make epilepsy surgery less invasive, safer, and in some cases more effective.This issue of Neurosurgical Focus contains articles representing the gamut of epilepsy surgery.Several papers highlight surgical techniques for specific pathologies and/or brain locations.Of particular technical interest is the excellent description of the paramedian supracerebellar approach for selective amygdalohippocampectomy.Another group of articles details the results of surgical series for various approaches and pathologies such as cavernomas, pediatric hemispherotomy, and temporal lobectomy in elderly patients.Several submissions focus on ways in which advanced neuroimaging contributes to selecting epilepsy surgery candidates.Other papers describe aspects of procedures new(er) to North American epilepsy surgery, including stereotactic laser ablation/ laser interstitial thermal therapy (LITT) and stereo-EEG (SEEG).Important research topics include the extension of SEEG recording to study the limbic thalamus in human epilepsy and the potential for interneuron transplantation as a human epilepsy therapy.This issue of Neurosurgical Focus represents an international sampling of many of the subject areas that make epilepsy surgery a technically challenging, progressively evolving, and scientifically fruitful field within neurosurgery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.021 |
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".