Supramarginal Resection of Metastatic Brain Tumors: A Meta-Analysis Study
Bibliographic record
Abstract
Background and Objectives: Over 30% of people who suffer from cancers are at risk of developing brain metastases. The typical recommended surgical therapy for metastases within the brain is gross total resection (GTR). Nevertheless, GTR solely may not always be adequate for disease management since remaining tumors can show local advancements and invasion. The focus of this research is to summarize the current data and to compare the outcomes of GTR and supramarginal resection. Materials and Methods: A search on the PubMed, Scopus, Cochrane Central Library, and Web of Science (WOS) databases was performed using specific keywords for single or multiple brain metastasis of any origin in patients who underwent either supramarginal resection or gross total resection. Results: The average age of the patients involved in the study spanned between 51 ± 6 years and 60.5 ± 10.1 years. Males represented 48.7% of the total population. The incidence of 1-year survival among the GTR group was 37.1%, whereas the supramarginal resection group showed an incidence of 91.3%, under the random effect model (0.551, 95% CI [0.18, 0.921]). The incidence of 2-year survival among the GTR group was 21.26%, whereas the supramarginal resection group showed an incidence of 72.46%, under the random effect model (0.380, 95% CI [0.113, 0.648]). The incidence of local recurrence among the GTR group was 57.69%, whereas the supramarginal resection group showed an incidence of 18.4%, under the random effect model (0.266, 95% CI [0.106, 0.426]). Conclusions: Supramarginal resection is a promising approach for the management of brain metastases.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.034 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".