Determining Surgical Candidacy in Temporal Lobe Epilepsy
Bibliographic record
Abstract
Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy that is amenable to surgical treatment. In the carefully selected patient, excellent seizure outcome can be achieved with minimal or no side effects from surgery. This may result in improved psychosocial functioning, achieving higher education, and maintaining or gaining employment. The objective of this paper is to discuss the surgical selection process of a patient with TLE. We define what constitutes a patient that has medically refractory TLE, describe the typical history and physical examination, and distinguish between mesial TLE and neocortical TLE. We then review the role of routine (ambulatory/sleep-deprived electroencephalography (EEG), video EEG, magnetic resonance imaging (MRI), neuropsychological testing, and Wada testing) and ancillary preoperative testing (positron emission tomography, single-photon emission computed tomography (SPECT), subtraction ictal SPECT correlated to MRI (SISCOM), magnetoencephalography, magnetic resonance spectroscopy, and functional MRI) in selecting surgical candidates. We describe the surgical options for resective epilepsy surgery in TLE and its commonly associated risks while highlighting some of the controversies. Lastly, we present teaching cases to illustrate the presurgical workup of patients with medically refractory TLE.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".