Awake Craniotomy: Controversies, Indications and Techniques in the Surgical Treatment of Temporal Lobe Epilepsy
Bibliographic record
Abstract
In 1886, Victor Horsley excised an epileptogenic posttraumatic cortical scar in a 23-year-old man under general anaesthesia and discussed his choice of anaesthesia: "I have not employed ether in operations on man, fearing that it would tend to cause cerebral excitement; chloroform, of course, producing on the contrary, well-marked depression." His concerns regarding anaesthesia are reiterated 100 years later as evidenced by the ongoing controversy over the choice of anaesthetic in surgical procedures for epilepsy. The current controversies regarding the necessity for local anaesthesia in temporal lobe epilepsy operations concern the utility of electrocorticography in surgical decision making, its relationship to seizure outcome and the value of intraoperative language mapping in dominant temporal lobe resections. The increasing sophistication of pre-operative investigation and localization of both areas of epileptogenesis and normal brain function and the introduction of minimally invasive surgical techniques and smaller focal resections are changing the indications for local anaesthesia in temporal lobe epilepsy. Thus, indications which were previously absolute are now perhaps relative. This article reviews the current indications for craniotomy under local anaesthesia in the surgical treatment of temporal lobe epilepsy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".