Outpatient Craniotomy for Brain Tumor: A Pilot Feasibility Study in 46 Patients
Bibliographic record
Abstract
BACKGROUND: Since 1991 the author has routinely performed awake craniotomy for intra-axial brain tumors with low complication rate and low resource utilization. In late 1996 a pilot study was initiated to assess the feasibility of performing craniotomy for tumor resection as an outpatient procedure. METHODS: A rigorous protocol was developed and adhered to, based around the patient's arrival at hospital at 6:00 a.m, undergoing image-guided awake craniotomy with cortical mapping, and being discharged by 6:00 p.m. RESULTS: During the 48 month period from December 1996 to December 2000, 245 awake craniotomies were performed and of those, 46 patients were entered into the outpatient craniotomy protocol. Pathology in the 46 intent-to-treat group was: 21 metastasis, 19 glioma, and six miscellaneous. Four patients required conversion to inpatients and one patient was readmitted later the same evening due to headache. Thus 41/46 patients successfully completed the protocol (89%). There were five complications in the 46 intent-to-treat group (10.9%). CONCLUSIONS: Outpatient craniotomy for brain tumor is a feasible option which appears safe and effective for selected patients. Besides being resource-friendly, the procedure may be psychologically less traumatic to patients than standard craniotomy for brain tumor. Proper prospective studies including satisfaction surveys would help resolve these issues and will be the next step.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".