Prospective study of awake craniotomy used routinely and nonselectively for supratentorial tumors
Bibliographic record
Abstract
OBJECT: The authors prospectively assessed the value of awake craniotomy used nonselectively in patients undergoing resection of supratentorial tumors. METHODS: The demographic features, presenting symptoms, tumor location, histological diagnosis, outcomes, and complications were documented for 610 patients who underwent awake craniotomy for supratentorial tumor resection. Intraoperative brain mapping was used in 511 cases (83.8%). Mapping identified eloquent cortex in 115 patients (22.5%) and no eloquent cortex in 396 patients (77.5%). RESULTS: Neurological deficits occurred in 89 patients (14.6%). In the subset of 511 patients in whom brain mapping was performed, 78 (15.3%) experienced postoperative neurological worsening. This phenomenon was more common in patients with preoperative neurological deficits or in those individuals in whom mapping successfully identified eloquent tissue. Twenty-five (4.9%) of the 511 patients suffered intraoperative seizures, and two of these individuals required intubation and induction of general anesthesia after generalized seizures occurred. Four (0.7%) of the 610 patients developed wound complications. Postoperative hematomas developed in seven patients (1.1%), four of whom urgently required a repeated craniotomy to allow evacuation of the clot. Two patients (0.3%) required readmission to the hospital soon after being discharged. There were three deaths (0.5%). CONCLUSIONS: Awake craniotomy is safe, practical, and effective during resection of supratentorial lesions of diverse pathological range and location. It allows for intraoperative brain mapping that helps identify and protect functional cortex. It also avoids the complications inherent in the induction of general anesthesia. Awake craniotomy provides an excellent alternative to surgery of supratentorial brain lesions in patients in whom general anesthesia has been induced.
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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 | 0.004 |
| 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) | 0.001 | 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".