Outpatient brain tumor surgery: innovation in surgical neurooncology
Bibliographic record
Abstract
OBJECT: Recent studies of conventional craniotomies and image-guided biopsies have afforded a solid characterization of surgical morbidity and the timing of its occurrence. This report outlines a novel 11-year experience with outpatient image-guided biopsy and outpatient craniotomy for supratentorial intraaxial brain tumors. METHODS: During the period between August 1996 and May 2007, 117 awake image-guided biopsies and 145 elective craniotomies for tumor resection were prospectively selected to be performed as outpatient procedures. Data were recorded for each patient regarding tumor histological type, reasons for admission if planned early discharge failed, and surgical complications. RESULTS: Successful discharge from the Day Surgery Unit was possible in 109 (93%) of 117 biopsy cases and 136 (94%) of 145 craniotomy cases (only 2 of which [1.5%] required unplanned readmission after discharge). Neurological worsening occurred in 5.1% of the patients who underwent image-guided biopsies, and in 5.5% of those who underwent outpatient craniotomies (based on intent-to-treat group analysis). No patient suffered an adverse event with alteration in outcome because of planned outpatient discharge. CONCLUSIONS: Outpatient image-guided brain biopsy and outpatient craniotomy for tumor resection are safe and effective procedures in selected patients.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".