Association between tamoxifen treatment and diabetes
Bibliographic record
Abstract
BACKGROUND: There is increasing evidence linking breast cancer and diabetes; however, few studies have explored the association between cancer treatments and risk of diabetes. Tamoxifen may increase diabetes incidence through its estrogen-inhibiting effects. This study assessed whether tamoxifen treatment in older breast cancer survivors is associated with an increased risk of diabetes. METHODS: This nested case-control study used population-based health databases in Ontario, Canada to identify women older than 65 years with early stage breast cancer between April 1, 1996 and March 31, 2006. Cases were defined as cohort members diagnosed with diabetes during follow-up (March 31, 2008), and each case was age-matched with up to 5 controls who did not develop diabetes. After adjusting for other risk factors, the authors compared the likelihood of diabetes between current tamoxifen users and tamoxifen nonusers, based on prescriptions at diabetes diagnosis. They also compared diabetes risk in current aromatase inhibitor users versus nonusers. RESULTS: Of 14,360 breast cancer survivors identified, mean age 74.9 years, 1445 (10%) developed diabetes over a mean follow-up of 5.2 years. Current tamoxifen therapy was associated with a significantly higher risk of diabetes compared with no tamoxifen therapy (adjusted odds ratio, 1.24; 95% confidence interval, 1.08-1.42; P = .002). There was no association between aromatase inhibitor therapy and diabetes. CONCLUSIONS: Current tamoxifen therapy is associated with an increased incidence of diabetes in older breast cancer survivors. These findings suggest that tamoxifen treatment may exacerbate an underlying risk of diabetes in susceptible women; further studies are needed to better explore this association.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".