Sodium–Glucose Cotransporter 2 Inhibitors and the Short-term Risk of Breast Cancer Among Women With Type 2 Diabetes
Bibliographic record
Abstract
Sodium–glucose cotransporter 2 (SGLT2) inhibitors are second- to third-line antidiabetes drugs that have been shown to have cardiovascular benefits. However, in premarketing trials of the SGLT2 inhibitor dapagliflozin, there were numerical imbalances in breast cancer events compared with placebo; all occurred within 1 year of randomization (rate ratio 2.47, 95% CI 0.64–14.10) (1). In contrast, no imbalances were observed in subsequent large cardiovascular outcome trials of dapagliflozin and other SGLT2 inhibitors (2–4). While the discrepant findings between the pre- and postmarketing trials could be due to chance, one hypothesis is that the early breast cancer imbalances are the result of an accelerated tumor-promoting effect of SGLT2 inhibitors. However, this is unlikely, given that sodium–glucose cotransporter proteins are not expressed in mammary tissue, and animal studies have failed to demonstrate that SGLT2 inhibitors have any neoplastic activity (5). Another hypothesis relates to the weight-lowering effects of SGLT2 inhibitors, which could facilitate the detection of existing breast lumps, thus leading to a transient overdetection of breast cancer. To date, this possible association has not been investigated in the real-world setting. We conducted a population-based cohort study using the U.K. Clinical Practice Research Datalink (CPRD) (protocol: 19_272). We identified all female patients newly treated with either an SGLT2 inhibitor (dapagliflozin, …
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".