Survival Benefit of Surgical Treatment Added Into Systemic Treatment for Isolated Breast Cancer Liver Metastases: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Compared with systemic treatment alone, whether surgical treatment combined with systemic treatment can improve survival outcomes of patients with isolated breast cancer liver metastases (BCLM) is still controversial. This meta-analysis was designed to evaluate the efficacy of surgical treatment for patients with isolated BCLM. Methods: A systematic search of PubMed, Embase, and Cochrane Library up to May 13, 2021 was conducted for relevant studies. The primary outcome was overall survival. The meta-analysis was performed using R software. The quality of the pooled study was assessed using the Newcastle-Ottawa scale. The publication bias was evaluated by funnel plots and Begg's and Egger's tests. Fixed- and random-effects models were applied according to heterogeneity. Results: 9 retrospective studies involving 13 cohorts (7 unmatched cohorts and 6 matched cohorts) were included in this study. The surgical cohorts had better overall survival than the systemic cohorts in the pooled analysis of all the included studies, in the subgroup analysis of liver resection, and in the subset of the matched cohorts. Conclusions: Compared with systemic treatment alone, surgical treatment combined with systemic treatment was proven to be associated with superior survival outcomes, which should be considered in selected patients with isolated BCLM.
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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) | 0.015 | 0.029 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".