Metformin Use and Kidney Cancer Survival Outcomes
Bibliographic record
Abstract
OBJECTIVES: Metformin has been associated with improved survival outcomes in various malignancies. However, studies in kidney cancer are conflicting. We performed a systematic review and meta-analysis to evaluate the association between metformin and kidney cancer survival. MATERIALS AND METHODS: We searched Medline and EMBASE databases from inception to June 2017 to identify studies evaluating the association between metformin use and kidney cancer survival outcomes. We evaluated risk of bias with the Newcastle-Ottawa scale. We pooled hazard ratios (HRs) for recurrence-free, progression-free, cancer-specific, and overall survival using random effects models, and explored heterogeneity with metaregression. We evaluated publication bias through Begg's and Egger's tests, and the trim and fill procedure. RESULTS: We identified 9 studies meeting inclusion criteria, collectively involving 7426 patients. Five studies were at low risk of bias. The direction of association for metformin use was toward benefit for recurrence-free survival (HR, 0.99; 95% confidence interval [CI], 0.36-2.74), progression-free survival (pooled HR, 0.84; 95% CI, 0.66-1.07), cancer-specific (pooled HR, 0.72; 95% CI, 0.48-1.09), and overall survival (pooled HR, 0.73; 95% CI, 0.50-1.09), though none reached statistical significance. Metaregression found no study-level characteristic to be associated with the effect size, and there was no strong evidence of publication bias for any outcome. CONCLUSIONS: There is no evidence of a statistically significant association between metformin use and any survival outcome in kidney cancer. We discuss the potential for bias in chemoprevention studies and provide recommendations to reduce bias in future studies evaluating metformin in kidney cancer.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".