Dietary Diabetes Risk Reduction Score (DDRRS) and Breast Cancer Risk: A Case-Control Study in Iran
Bibliographic record
Abstract
INTRODUCTION: Given the role of type 2 diabetes and insulin resistance in tumor initiation, we hypothesized that following a diet that reduces the risk of type 2 diabetes could also reduce the risk of breast cancer. Herein, we conducted a case-control study to investigate the association between dietary diabetes risk reduction score and breast cancer risk in Iranian women. METHOD: We recruited 136 newly diagnosed cases and 272 age-matched hospitalized controls from referral hospitals. A valid and reliable 168-item food frequency questionnaire (FFQ) was used to collect the data on dietary intake. We used Multivariate Logistic regression to assess the odds ratio (OR) with 95% confidence interval (CI) of breast cancer by the dietary diabetes risk reduction score tertiles. RESULTS: After adjusting for confounding variables, no association was seen between dietary diabetes risk reduction score and breast cancer risk (OR = 0.65, 95% CI: 0.37-1.14). However, after stratification by menopausal status, a decreased risk was observed between adherence to dietary diabetes risk reduction score and breast cancer risk in postmenopausal individuals (OR = 0.43, 95% CI: 0.19-0.99). CONCLUSION: The present study states that there is no significant relationship between the dietary diabetes risk reduction score and the risk of breast cancer. However, adherence to dietary diabetes risk reduction score could have a preventive role for breast cancer among postmenopausal women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".