Reclassifying Menopausal Breast Cancer and Assessing Non-Genetic Risk Factors in Ghanaian Women: Insights from a Cohort Study
Bibliographic record
Abstract
Background/Objectives: Breast cancer incidence is increasing in younger Ghanaian women. However, few epidemiological studies have evaluated the modifiable risk factors in this population. Additionally, these studies have classified breast cancer in Ghanaian women based on the global menopausal case classification. This study reclassified premenopausal and postmenopausal breast cancer in a Ghanaian cohort, assessing the risk factors using the observed menopausal age in Ghanaian women of 48 years, rather than the global standard of 50 years. Methods: Women di-agnosed with breast cancer and scheduled for surgery from December 2018 to March 2023 were recruited across four hospitals in Ghana for the Ghana Breast Cancer Omics Project (BCOPGh), and data were collected using a questionnaire. Cross-tabulation and linear regression were used to evaluate the relationships between categorical variables and age at diagnosis. Results: Out of a total of 262 women recruited, 34.4% were classified as having premenopausal breast cancer; while early-onset breast cancer (EOBC) accounted for 14.9% of all cases. The molecular subtypes were predominantly Hormone receptor (HR)-positive (61%) while triple-negative breast cancer (TNBC) accounted for 16%. The tumours were predominantly at stage II (62%) and grade 2 (51%) with invasive carcinoma NST (56%) being the most common subtype. Within this cohort, nulliparity increased EOBC risk by 13.5-fold, while having a first birth after the age of 23 doubled the odds of premenopausal breast cancer. Reproductive factors (menarche and menopause) and lifestyle (al-cohol intake, smoking, contraceptive use, and breastfeeding duration) were not associated with premenopausal breast cancer in this cohort. About 13% of participants reported a family history of breast cancer, and 79% had prior knowledge of the disease. Conclusion: This study supports pre-vious reports of relatively higher incidence of aggressive disease in young Ghanaian women and the protective effect of early age at first birth. It further underscores the need to investigate its genetic underpinnings, whilst highlighting the importance of public education on self-examination tech-niques to reduce advanced disease presentation in Ghanaian 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".