[Risk factors of breast cancer in Asian women: a meta-analysis].
Bibliographic record
Abstract
OBJECTIVE: To evaluate the risk factors of breast cancer in Asian women and to provide evidences for establishing a risk assessment model. METHODS: Published studies concerning risk factors of breast cancer in Asian women were searched systemically and assessed by NOS (Newcastle-Ottawa Scale) items between 1995 and 2010. RevMan 4.2 software was used for data analysis and for calculating OR and its 95%CI on every risk factor. RESULTS: 27 studies including 403 170 women were selected for Meta-analysis. According to NOS items, 20 studies were classified as A degree and 7 studies were evaluated as B degree. The risk factors of breast cancer and its pooled odds ratio values with statistical significance were as follows: 3.00 (95%CI: 1.68 - 5.36) when number of abortions ≥ 3; 2.39 (95%CI: 1.78 - 3.21) when with family history of breast cancer; 1.54 (95%CI: 1.30 - 1.82) when age at first live birth ≥ 30 (year); smoking was 1.50 (95%CI: 1.03 - 2.20); 1.48 (95%CI: 1.20 - 1.83) with no live births; 1.29 (95%CI: 1.12 - 1.47) with no breast feeding; 1.26 (1.07 - 1.49) with age at menarche ≤ 12 (year) and 1.16 (95%CI: 1.01 - 1.32) with alcohol drinking. CONCLUSION: Number of abortions ≥ 3, family history of breast cancer, age at first live birth ≥ 30 (year), smoking, no live births, no breast feeding, age at menarche ≤ 12 (year), and alcohol drinking were among the priorities in the establishment of breast cancer risk assessment model for Asian 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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.054 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".