Awake Craniotomy for Removal of Intracranial Tumor: Considerations for Early Discharge
Bibliographic record
Abstract
UNLABELLED: We retrospectively reviewed the anesthetic management, complications, and discharge time of 241 patients undergoing awake craniotomy for removal of intracranial tumor to determine the feasibility of early discharge. The results were analyzed by using univariate analysis of variance and multiple logistic regression. The median length of stay for inpatients was 4 days. Fifteen patients (6%) were discharged 6 h after surgery and 76 patients (31%) were discharged on the next day. Anesthesia was provided by using local infiltration supplemented with neurolept anesthesia consisting of midazolam, fentanyl, and propofol. There was no significant difference in the total amount of sedation required. Overall, anesthetic complications were minimal. One patient (0.4%) required conversion to general anesthesia and one patient developed a venous air embolus. Fifteen patients (6%) had self-limiting intraoperative seizures that were short-lived. Of the 16 patients scheduled for ambulatory surgery, there was one readmission and one unanticipated admission. It may be feasible to discharge patients on the same or the next day after awake craniotomy for removal of intracranial tumor. However, caution is advised and patient selection must be stringent with regards to the preoperative functional status of the patient, tumor depth, surrounding edema, patient support at home, and ease of access to hospital for readmission. IMPLICATIONS: It may be feasible to perform awake craniotomies for removal of intracranial tumor as an ambulatory procedure; however, caution is advised. Patient selection must be stringent with respect to the patient's preoperative functional status, tumor depth, surrounding edema, patient support at home, and ease of access to hospital for readmission.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".