Breast Cancer in Systemic Lupus Erythematosus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.002 |
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 it