Insulin, Macronutrient Intake, and Physical Activity: Are Potential Indicators of Insulin Resistance Associated with Mortality from Breast Cancer?
Bibliographic record
Abstract
High levels of insulin have been associated with increased risk of breast cancer, and poorer survival after diagnosis. Data and sera were collected from 603 breast cancer patients, including information on diet and physical activity, medical history, family history, demographic, and reproductive risk factors. These data were analyzed to test the hypothesis that excess insulin and related factors are directly related to mortality after a diagnosis of breast cancer. The cohort was recruited from breast cancer patients treated at the British Columbia Cancer Agency between July 1991 and December 1992. Questionnaire and medical record data were collected at enrollment and outcomes were ascertained by linkage to the BC Cancer Registry after 10 years of follow-up. The primary outcome of interest was breast cancer-specific mortality (n = 112). Lifestyle data were analyzed using Cox proportional hazards regression models to relate risk factors to outcomes, controlling for potential confounders, such as age and stage at diagnosis. Data for biological variables were analyzed as a nested case-control study due to limited serum volumes, with at least one survivor from the same cohort as a control for each breast cancer death, matched on stage and length of follow-up. High levels of insulin were associated with poorer survival for postmenopausal women [odds ratio, 1.9; 95% confidence interval (CI), 0.7-6.6, comparing highest to lowest tertile, P trend = 0.10], while high dietary fat intake was associated with poorer survival for premenopausal women (relative risk, 4.8; 95% CI, 1.3-18.1, comparing highest to lowest quartile). Higher dietary protein intake was associated with better survival for all women (relative risk, 0.4; 95% CI, 0.2-0.8, comparing highest to lowest quartile).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".