Influence of Preexisting Diabetes on Survival After a Breast Cancer Diagnosis in First Nations Women in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: Survival after a breast cancer diagnosis is poorer in First Nations women with a preexisting comorbidity compared with comorbidity-free First Nations women in Ontario, Canada. Given the high prevalence of diabetes in this population, it is important to determine whether preexisting diabetes is related to poorer survival after a breast cancer diagnosis. METHODS: All First Nations women were identified from a cohort of First Nations people diagnosed with breast cancer in diagnostic periods-1995 to 1999 and 2000 to 2004-and seen at a regional cancer program (RCP) in Ontario. Preexisting diabetes status and other factors, such as age at diagnosis, body mass index, and stage at diagnosis, were collected from medical charts at the regional cancer programs. The association between preexisting diabetes and First Nations status was examined by each of the demographic, personal, tumor, and treatment factors using logistic regression models. Survival was compared between First Nations women with (n = 67) and without (n = 215) preexisting diabetes, adjusted by significant study factors using a Cox proportional hazards regression model. RESULTS: < .01). Preexisting diabetes significantly increased the risk of death among First Nations women with breast cancer (hazard ratio, 1.87; 95% CI, 1.12 to 3.13) after adjustment for age group, period of diagnosis, body mass index, other comorbidities at diagnosis, and stage. CONCLUSION: This study recommends awareness of this survival discrepancy among the treatment team for First Nations patients with breast cancer with preexisting diabetes.
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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".