Association between Metformin Use and Mortality after Cervical Cancer in Older Women with Diabetes
Bibliographic record
Abstract
BACKGROUND: To examine the association between metformin use and mortality in patients with diabetes and cervical cancer. METHODS: Using Ontario health databases, a retrospective, population-based cohort study was conducted in women with diabetes ≥ age 66 years diagnosed with cervical cancer between 1997 and 2010. The association between metformin exposure and cervical cancer-specific mortality was examined using Fine-Gray regression models, with noncancer death as a competing risk and cumulative metformin use as a time-varying exposure. The association with overall mortality was examined using Cox regression models. RESULTS: Among the 181 women with diabetes and cervical cancer, there were 129 deaths, including 61 cervical cancer-specific deaths. The median follow-up was 5.8 years (interquartile range 4.2-9.6 years) for surviving patients. Cumulative dose of metformin after cervical cancer diagnosis was independently associated with a decreased risk of cervical cancer-specific mortality and overall mortality in a dose-dependent fashion [HR 0.79; 95% confidence interval (CI), 0.63-0.98; and HR 0.95; 95% CI, 0.90-0.996 per each additional 365 g of metformin use, respectively]. There was no significant association between cumulative use of other antidiabetic drugs and cervical cancer-specific mortality. CONCLUSION: This study suggests an association between cumulative metformin use after cervical cancer diagnosis and lower cervical cancer-specific and overall mortality among older women with diabetes. IMPACT: Cumulative dose of metformin use after cervical cancer diagnosis among older women with diabetes may be associated with a significant decrease in mortality. This finding has important implications if validated prospectively, as metformin is inexpensive and can be easily combined with standard treatment for cervical 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".