Reproductive risk factor patterns in the Caribbean affecting breast cancer age of onset.
Bibliographic record
Abstract
e13632 Background: Breast cancer is the leading cause of cancer in high-income (HIC) and low to middle-income countries (LMIC); and is the most common diagnosed cancer among women. Factors affecting breast cancer incidence include obesity, parity, age of menarche, age of first pregnancy and mutations in BRCA1/2 and genes involved in the homologous recombination repair pathway. These factors differ between HICs and LMICs including countries in the Caribbean. The majority of women from the Caribbean are of African ancestry and Black women have increased morbidity and mortality rates of breast cancer. Our goal is to study how reproductive patterns affect breast cancer age at presentation in Caribbean-born women. Methods: We conducted a prospective observational study recruiting patients from The Bahamas, Barbados, Cayman Islands, Dominica, Haiti, Jamaica and Trinidad and Tobago. Women were considered eligible if they were diagnosed with primary breast cancer at any age. The cohort was divided into four groups based on the year of birth ( < 1950, 1950 – 1959, 1960 – 1969, > 1970). The following data was collected: age at diagnosis of breast cancer, family history of cancer, age of first pregnancy, number of pregnancies, number of children, number of siblings, BMI at time of enrollment, age of menarche and menopause. Data analysis was conducted using the Chi-square test, ANOVA and logistic regression model. Results: A total of 1015 were enrolled and 995 met inclusion criteria. When comparing women born < 1950 to those > 1970, there was a statistically significant difference between means for the variables: Age at Breast cancer diagnosis (60.7 vs 35, p <.0001); number of siblings (4.5 vs 6.6, p <.0001); age of menarche (13.4 vs 12.3, p <.0001); number of pregnancies (4 vs 2.09); number of children (3.5 vs 1.6, p <.0001) and age of menopause at diagnosis (47.4 vs 37.5, p <.0001). When comparing women who were never pregnant to those who experienced pregnancy, mean age at breast cancer diagnosis was significantly reduced from 47.3 to 41.5 (p < 0.0001). Women who had n = 3 or more children were diagnosed older (mean of 49.9 years), compared to women with 2 or less children, (45.1 years, p < 0.0001). In multivariate analysis a higher likelihood for a positive mutation between year of birth 1960-1969 (aOR 2.19 [1.24 – 3.88], p = 0.007) and > 1970 (aOR 2.02 [1.06 – 3.88], p = 0.034) compared to < 1950. Conclusions: Our data shows that the Caribbean has undergone a rapid change in reproductive patterns in one generation. These changes provide an insight of risk factor patterns for breast cancer incidence which are associated with younger age of onset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 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".