Structural Determinants of Childbearing Challenges in Breast Cancer Survivorship: A Systematic Review
Bibliographic record
Abstract
Objectives: A growing number of women of reproductive age are battling breast cancer. The younger the age of the breast cancer, the more aggressive gonadal toxic treatments are needed. A large number of younger women with breast cancer are childless and plan to become pregnant after treatment. Thus, this study aimed to understand the structural determinants of health associated with the challenges these women face as they navigate childbearing after breast cancer treatment. Methods: PubMed, Scopus, Embase and Web of Science were searched up to July 2024. The review was preregistered (PROSPERO: CRD42024502269). We used "Newcastle-Ottawa Scale" for risk of bias assessment of studies. Results: A total of nine studies met the inclusion criteria. They were all of high quality and had little chance of being biased. Breast cancer survivors’ reproductive choices and childbearing are influenced by a number of structural determinants of health. These determinants include age, education level, socioeconomic status, housing, race, and ethnicity. Since age was a determinant in seven of eight studies reviewed (the lower the age, the greater the childbearing intention, fertility preservation, counseling, and pregnancy attempt), age appeared to be a more significant and influential factor. Conclusions: This review analysis revealed a connection between the reproductive practices of surviving women and the structural determinants of health and fertility. Reproductive-aged women who have struggled with this condition in the past may face various difficulties because of their fertility problems. Therefore, it seems beneficial to understand these factors and develop strategies to address these obstacles. This will allow these women to live a happy and hopeful life.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".