Risk Factors for Premenopausal Breast Cancer in Bangladesh
Bibliographic record
Abstract
Background. The incidence of premenopausal breast cancer is rising throughout South Asia. Our objective was to determine the role of risk factors associated with Westernization for premenopausal breast cancer in Bangladesh. Methods. We conducted a matched case-control study between January 1, 2007, and December 31, 2010, at four hospitals in Bangladesh. Cases were premenopausal women diagnosed with invasive breast cancer. Controls were premenopausal women with no personal history of breast cancer. Logistic regression was used to calculate the odds ratios (OR) for breast cancer. Results. We identified 129 age-matched pairs. The mean age of breast cancer diagnosis was 37.5 years. Each year decrease in the age of menarche significantly increased the risk of breast cancer (OR = 1.67, 95% CI 1.09-2.56, P = 0.02). The risk was also increased with a current body mass index of ≥25 kg/m(2) (OR = 5.24, 95% CI 1.10-24.9, P = 0.04). Age at first childbirth, parity, and breastfeeding were not significantly associated with premenopausal breast cancer risk (P > 0.05). Conclusions. Age at menarche and adult weight gain were associated with premenopausal breast cancer risk. Other factors associated with Westernization may not be relevant to premenopausal breast cancer risk in Bangladesh.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".