Breast cancer in systemic lupus
Bibliographic record
Abstract
Objective There is a decreased breast cancer risk in systemic lupus erythematosus (SLE) versus the general population. We assessed a large sample of SLE patients, evaluating demographic and clinical characteristics and breast cancer risk. Methods We performed case-cohort analyses within a multi-center international SLE sample. We calculated the breast cancer hazard ratio (HR) in female SLE patients, relative to demographics, reproductive history, family history of breast cancer, and time-dependent measures of anti-dsDNA positivity, cumulative disease activity, and drugs, adjusted for SLE duration. Results There were 86 SLE breast cancers and 4498 female SLE cancer-free controls. Patients were followed on average for 7.6 years. Versus controls, SLE breast cancer cases tended to be white and older. Breast cancer cases were similar to controls regarding anti-dsDNA positivity, disease activity, and most drug exposures over time. In univariate and multivariate models, the principal factor associated with breast cancers was older age at cohort entry. Conclusions There was little evidence that breast cancer risk in this SLE sample was strongly driven by any of the clinical factors that we studied. Further search for factors that determine the lower risk of breast cancer in SLE may be warranted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".