Relationship between pregnancy-associated variables and breast cancer risk: A systematic review
Bibliographic record
Abstract
Introduction: Globally, breast cancer is the most common malignancy causing extensive mortality in women. In most cases, there is no known risk factor for breast cancer. Extensive hormonal changes that occur during pregnancy appear to be involved in the etiology of breast cancer. Since identifying risk factors helps with primary prevention and early diagnosis of breast cancer, this study was performed to systematically review studies about pregnancy-related variables and the risk of breast cancer in Iran and worldwide. Methods: In this systematic review, we searched PubMed, Google Scholar, Scopus, SID, and Science Direct databases using keywords of “pregnancy-related variables” and “breast cancer” to retrieve articles published during 2000-2017. Then, those articles that obtained a score of ≥ 6 based on the Newcastle–Ottawa Scale were entered to the study. The results were reported qualitatively. Results: Fifty articles consisting of 26 case-controls and 24 cohort articles, which met the inclusion criteria, were investigated. Our evaluations indicated that among the factors examined, abortion and preeclampsia had a stronger relationship with breast cancer. Conclusion: History of abortion seems to increase the risk of breast cancer. Also, history of preeclampsia plays a protective role in breast cancer. Further studies are needed to examine the relationship of gestational diabetes and multiple gestations with breast cancer.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".