The Impact of Diabetes on Breast Cancer Treatments and Outcomes: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: Women with breast cancer and diabetes face worse outcomes than those with breast cancer without diabetes; however, the contribution of comorbidity to these disparities remains unclear. We evaluated the impact of diabetes on receipt of cancer treatments as well as mortality while accounting for other comorbidities. RESEARCH DESIGN AND METHODS: Ontario administrative databases were used to compare the rate of receipt of breast cancer treatments between women with and without diabetes. We also performed adjusted cause-specific hazard models to account for comorbidities when evaluating differences in treatments received and mortality outcomes between the two groups. RESULTS: Women with diabetes and stage III breast cancer were slightly less likely to receive chemotherapy (relative risk [RR] 0.93 [95% CI 0.89-0.97]), although this difference was not significant when we adjusted for comorbidities (adjusted hazard ratio [aHR] 1.03 [95% CI 0.93-1.13]). We saw similar trends for receipt of guideline-adherent radiotherapy (RR 0.97 [0.95-0.99], aHR 0.98 [0.94-1.02]). All-cause mortality was increased in women with diabetes after adjusting for comorbidities (aHR 1.16 [1.06-1.27]), but breast cancer-specific mortality was not increased overall. Women with a longer duration of diabetes and those with preexisting cardiovascular disease had increased all-cause and cancer-specific mortality. CONCLUSIONS: Although cancer treatments received were similar between women with and without diabetes, breast cancer-specific mortality remains higher among women with diabetes who have longer diabetes duration or preexisting cardiovascular disease. This study uncovers new information about key risk factors for poorer prognosis in women with diabetes and breast cancer.
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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".