Metformin and Breast Cancer Stage at Diagnosis: A Population-Based Study
Bibliographic record
Abstract
PURPOSE: The objective of the present study was to use a large, population-based cohort to examine the association between metformin and breast cancer stage at diagnosis while accounting for mammography differences. METHODS: We used data from Ontario administrative health databases to identify women 68 years of age or older with diabetes and invasive breast cancer diagnosed from 1 January 2007 to 31 December 2012. Adjusted logistic regression models were used to compare breast cancer stage at diagnosis (stages i and ii vs. iii and iv) between the women exposed and not exposed to metformin. We also examined the association between metformin use and estrogen receptor status, tumour size, and lymph node status in the subset of women for whom those data were available. RESULTS: We identified 3125 women with diabetes and breast cancer; 1519 (48.6%) had been exposed to metformin before their cancer diagnosis. Median age at breast cancer diagnosis was 76 years (interquartile range: 72-82 years), and mean duration of diabetes was 8.8 ± 5.9 years. In multivariable analyses, metformin exposure was not associated with an earlier stage of breast cancer (odds ratio: 0.98; 95% confidence interval: 0.81 to 1.19). In secondary analyses, metformin exposure was not associated with estrogen receptor-positive breast cancer, tumours larger than 2 cm, or positive lymph nodes. CONCLUSIONS: This population-based study did not show an association between metformin use and breast cancer stage or tumour characteristics at diagnosis. Our study considered older women with long-standing diabetes, and therefore further studies in younger patients could be warranted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".