Surgical resection for multiple brain metastases: a systematic review and meta-analysis of functional and survival outcomes
Bibliographic record
Abstract
Background: Brain metastases (BMs) are the most common intracranial tumors among adults, which exceed primary brain tumors by far. Surgery and radiotherapy represent the key local management of BM. However, the exact role of surgery is still under debate. Objective: To comprehensively evaluate the safety and efficacy of surgical management in patients with brain metastases. Methods: We searched four electronic databases from January 2023 until September 2024 (PubMed, Scopus, Web of Science, and Cochrane Library). All the studies assessing the role of surgery in managing BM were included. Our primary search targets were survival, mortality, and postoperative Karnofsky Performance Status (KPS). The results were reported as pooled mean or proportions with 95% confidence interval (CI) for continuous and dichotomous data, respectively. Results: Eight observational studies comprising 1010 patients met our inclusion criteria. The pooled mean of overall survival was 10.482 with 95% CI [7.651, 13.314]. While the pooled proportion of one-year and two-year survival was (0.451, 95% CI [0.320, 0.582]) and (0.240, 95% CI [0.112, 0.367]), respectively. We found the pooled proportion of overall mortality to be 0.535 with 95% CI [0.278, 0.793]. Patients with immediate postoperative KPS improvement showed a pooled estimate of 0.463 with 95% CI [0.243, 0.683]. Conclusion: Surgical resection is an effective therapeutic option for patients with BMs. Yet, careful patient selection and surgical technique are crucial for reducing postoperative complications and death.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".