The role of lobectomy in glioblastoma management: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Lobectomy has recently been employed in the management of glioblastoma (GB). Compared to subtotal, gross total and supramarginal resection, lobectomy provides maximum cytoreduction and improves overall survival (OS). Research question: The primary aim of this study is to compare lobectomy to other techniques for managing GB in terms of OS and progression-free survival (PFS). This study evaluated the association of the available surgical techniques for GB management with the reported relevant seizure outcome, operation time, length of stay, complication incidence, and Karnofsky performance status. Materials and methods: A PRISMA-compliant systematic review and meta-analysis was performed. We searched PubMed, Scopus, and Web of Science from January 2013 until April 2023. Random-effects models were employed. The Newcastle-Ottawa scale (NOS) and the GRADE approach were used for estimating risk of bias and quality of evidence. Results: We included six studies. Lobectomy demonstrated a mean OS of 25 months, compared to 13.72 months for gross total resection (GTR), and a PFS of 16.13 months, compared to 8.77 months for GTR. Comparing lobectomy to GTR, no statistically significant differences were observed regarding seizure management, length of stay, operation time, complications, and KPS due to limited amount of data. Discussion and conclusion: Our analysis demonstrated that lobectomy compared to GTR has a tremendous impact on the OS and the PFS, which seems to be improved almost by a year. Lobectomy, while demanding from a technical standpoint, constitutes a safe surgical procedure but further studies should assess its exact role in the management of GB 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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.006 | 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".