Hormonal exposures and breast cancer in a sample of women with systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVES: To determine if breast cancer risk in women with SLE is modified by a history of exposure to hormone replacement therapy (HRT) or oral contraceptives (OC), after adjusting for other risk factors. METHODS: Data were pooled from SLE cohorts at three centres. For each female cohort member (n = 871), the probability of developing breast cancer was estimated from factors (age, parity, age at first live birth, age of menarche, personal history of benign breast disease, family history) in the Gail model, an established tool for predicting breast cancer risk. From these probabilities, the expected number of breast cancers for the cohort was estimated. Actual occurrence of cases was determined by linkage with regional cancer registries. Standardized incidence ratios (SIRs; ratio of cancers observed to expected) were calculated, with subgroup analyses according to HRT and OC exposure. RESULTS: In the cohort, 15 breast cancers occurred vs 7.2 predicted [SIR 2.1, 95% confidence interval (CI) 1.1, 3.5]. When controlling for Gail model risk factors, estimates were similar for women never exposed to HRT vs those exposed to HRT. Adjusted SIR estimates appeared similar also for women exposed or not exposed to OC. CONCLUSIONS: Although not definitive, the data suggest that the breast cancer experience in this sample is not completely explained by factors such as reproductive and family history, or by exogenous hormonal exposures. Other determinants, including medication exposures or genetic factors (possibly related to oestrogen receptors or metabolism) may be important. Variations in these factors might explain why an elevated risk of breast cancer has not been apparent in all SLE populations.
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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".