Metformin Use and All-Cause and Prostate Cancer–Specific Mortality Among Men With Diabetes
Bibliographic record
Abstract
PURPOSE: To evaluate the association between cumulative duration of metformin use after prostate cancer (PC) diagnosis and all-cause and PC-specific mortality among patients with diabetes. PATIENTS AND METHODS: We used a population-based retrospective cohort design. Data were obtained from several Ontario health care administrative databases. Within a cohort of men older than age 66 years with incident diabetes who subsequently developed PC, we examined the effect of duration of antidiabetic medication exposure after PC diagnosis on all-cause and PC-specific mortality. Crude and adjusted hazard ratios (HRs) were calculated by using a time-varying Cox proportional hazard model to estimate effects. RESULTS: The cohort consisted of 3,837 patients. Median age at diagnosis of PC was 75 years (interquartile range [IQR], 72 to 79 years). During a median follow-up of 4.64 years (IQR, 2.7 to 7.1 years), 1,343 (35%) died, and 291 patients (7.6%) died as a result of PC. Cumulative duration of metformin treatment after PC diagnosis was associated with a significant decreased risk of PC-specific and all-cause mortality in a dose-dependent fashion. Adjusted HR for PC-specific mortality was 0.76 (95% CI, 0.64 to 0.89) for each additional 6 months of metformin use. The association with all-cause mortality was also significant but declined over time from an HR of 0.76 in the first 6 months to 0.93 between 24 and 30 months. There was no relationship between cumulative use of other antidiabetic drugs and either outcome. CONCLUSION: Increased cumulative duration of metformin exposure after PC diagnosis was associated with decreases in both all-cause and PC-specific mortality among diabetic men.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".