Awake craniotomy for resection of supratentorial glioblastoma: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The goal of glioblastoma (GBM) surgery is to maximize the extent of resection (EOR) while minimizing postoperative neurological complications. Awake craniotomy (AC) has been demonstrated to achieve this goal for low-grade gliomas in or near eloquent areas. However, the efficacy of AC for GBM resection has not been established. Therefore, we aimed to investigate the outcomes of AC for surgical resection of GBM using a systematic review and meta-analysis of published studies. METHODS: Systematic searches of Ovid MEDLINE, Embase, Cochrane Controlled Register of Controlled Trials, and PubMed were performed from database inception to September 14, 2019 for published studies reporting outcomes of AC for GBM resection. Outcome measures analyzed included EOR and the event rate of postoperative neurological deficits. RESULTS: A total of 1928 unique studies were identified. Fourteen studies reporting 278 patients were included in our meta-analysis. Mean age of patients was 46.9 years (95% confidence interval [CI]: 43.9-49.9). Early and late postoperative neurological deficits occurred in 34.5% (95% CI: 21.9-48.2) and 1.9% (95% CI: 0.0-9.2) of patients, respectively. Pooled percentage of gross total resection (GTR) was 74.7% (95% CI: 66.7-82.1), while the pooled percentage reduction in tumor volume was 95.3% (95% CI: 92.2-98.4). CONCLUSIONS: Limited current evidence suggests that the use of AC for resection of supratentorial GBM is associated with a low rate of persistent neurological deficits while achieving an acceptable rate of GTR. Our findings demonstrate the potential viability of AC in GBM resection and highlight the need for further research on this topic.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".