The Use of Metformin in Patients with Prostate Cancer and the Risk of Death
Bibliographic record
Abstract
BACKGROUND: Given the conflicting results from observational studies, we assessed whether the use of metformin after a prostate cancer diagnosis is associated with a decreased risk of cancer-specific and all-cause mortality. METHODS: This study was conducted linking four databases from the United Kingdom. A cohort of men newly diagnosed with nonmetastatic prostate cancer with a history of treated type II diabetes, between April 1, 1998 and December 31, 2009, was followed until October 1, 2012. Nested case-control analyses were performed for cancer-specific mortality and all-cause mortality, in which exposure was defined as use of metformin during the time to risk-set. Conditional logistic regression was used to estimate adjusted rate ratios (RR) of each outcome with 95% confidence intervals (CI). RESULTS: The cohort consisted of 935 men with prostate cancer and a history of type II diabetes. After a mean follow-up of 3.7 years, 258 deaths occurred, including 112 from prostate cancer. Overall, the post-diagnostic use of metformin was not associated with a decreased risk of cancer-specific mortality (RR, 1.09; 95% CI, 0.51-2.33). In a secondary analysis, a cumulative duration ≥938 days was associated with an increased risk (RR, 3.20; 95% CI, 1.00-10.24). The post-diagnostic use of metformin was not associated with all-cause mortality (RR, 0.79; 95% CI, 0.50-1.23). CONCLUSION: The use of metformin after a prostate cancer diagnosis was not associated with an overall decreased risk of cancer-specific and all-cause mortality. IMPACT: The results of this study do not support a role for metformin in the prevention of prostate cancer outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".