Breast Cancer in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Evidence points to a decreased breast cancer risk in systemic lupus erythematosus (SLE). We analyzed data from a large multisite SLE cohort, linked to cancer registries. METHODS: Information on age, SLE duration, cancer date, and histology was available. We analyzed information on histological type and performed multivariate logistic regression analyses of histological types according to age, SLE duration, and calendar year. RESULTS: We studied 180 breast cancers in the SLE cohort. Of the 155 cases with histology information, 11 were referred to simply as 'carcinoma not otherwise specified'. In the remaining 144 breast cancers, the most common histological type was ductal carcinoma (n = 95; 66%) followed by lobular adenocarcinoma (n = 11; 8%), 15 cancers were of mixed histology, and the remaining ones were special types. In our regression analyses, the independent risk factors for lobular versus ductal carcinoma was age [odds ratio (OR) 1.07, 95% confidence interval (CI) 1.01-1.14] and for the 'special' subtypes it was age (OR 1.06, 95% CI 1.01-1.10) and SLE duration (OR 1.05, 95% CI 1.00-1.11). CONCLUSIONS: Generally, up to 80% of breast cancers are ductal carcinomas. Though our results are not definitive, in the breast cancers that occur in SLE, there may be a slight decrease in the ductal histological type. In our analyses, age and SLE duration were independent predictors of histological status.
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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 | 0.001 | 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) | 0.003 | 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".