The Effect of Metformin on Mortality Following Cancer among Patients with Diabetes
Bibliographic record
Abstract
Diabetes may be a risk factor for cancer and is associated with worse cancer outcomes. Metformin may reduce cancer risk; however, its effect on mortality following cancer remains less clear. EMBASE and Medline were searched through February 10, 2014, for studies reporting an adjusted risk estimate for the effect of metformin therapy on mortality following cancer among diabetic patients. Random-effects models were used to obtain summary HR for the association between metformin and all-cause and cancer-specific mortality. Twenty-one observational studies were meta-analyzed in the primary analysis. Metformin was associated with a reduction in all-cause mortality [HR, 0.73; 95% confidence intervals (CI), 0.64-0.83] and cancer-specific mortality (HR, 0.74; 95% CI, 0.62-0.88). Subgroup analyses by cancer site showed a significant reduction in mortality for colon cancer (four studies, HR, 0.65; 95% CI, 0.56-0.76) but not for breast and prostate cancers. Observational studies indicate that metformin exposure at cancer diagnosis may be associated with a reduction in mortality. However, these findings need to be interpreted with caution as methodologic limitations of individual studies may have introduced biases in these findings. Our results emphasize the need for well-designed studies to further understand the relationship between metformin and survival following 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".