ASSOCIATION OF METFORMIN WITH BREAST CANCER INCIDENCE AND MORTALITY: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Background Preclinical data suggests that metformin may have anti-cancer effects to reduce breast cancer incidence and improve cancer prognosis. However, the current evidence in observational studies is inconclusive. A systematic review and meta-analysis was conducted to assess the effect of metformin on the incidence and mortality of breast cancer in diabetic patients. Methods A comprehensive literature search was performed on Medline (Pubmed), EMBASE, and the Cochrane library from inception to November 2016 with no language restrictions. Outcomes were incidence of breast cancer and all-cause mortality. Risk of bias and overall quality of evidence was assessed using the Newcastle Ottawa Scale and GRADE respectively. A meta-analysis was performed using the most adjusted odds ratios (ORs) or hazard ratios (HRs) and 95% confidence intervals (95% CI) as effect measures. Results A total of 12 observational studies were included for breast cancer incidence and 11 studies for all-cause mortality. No significant association was found between metformin exposure and incidence of breast cancer (OR: 0.93, 95% CI: 0.85-1.03, I2 = 35%). A 45% risk reduction was observed for all-cause mortality (HR: 0.55, 95%CI: 0.44-0.70, I2=81%). Presence of publication bias is strongly suspected for both outcomes. Conclusion The use of metformin in standard cancer therapy may improve overall survival of diabetic patients with breast cancer. No effect of metformin on the incidence of breast cancer was observed. Interpretation of results is limited by the observational nature of the studies and methodological biases. Clinical trials are warranted to determine the role of metformin in breast cancer risk reduction and prognosis.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| 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".