Cancer Risk in a Large Inception Systemic Lupus Erythematosus Cohort: Effects of Demographic Characteristics, Smoking, and Medications
Bibliographic record
Abstract
OBJECTIVE: To assess cancer risk factors in incident systemic lupus erythematosus (SLE). METHODS: Clinical variables and cancer outcomes were assessed annually among incident SLE patients. Multivariate hazard regression models (overall risk and most common cancers) included demographic characteristics and time-dependent medications (corticosteroids, antimalarial drugs, immunosuppressants), smoking, and the adjusted mean Systemic Lupus Erythematosus Disease Activity Index 2000 score. RESULTS: Among 1,668 patients (average 9 years follow-up), 65 cancers occurred: 15 breast, 10 nonmelanoma skin, 7 lung, 6 hematologic, 6 prostate, 5 melanoma, 3 cervical, 3 renal, 2 each gastric, head and neck, and thyroid, and 1 each rectal, sarcoma, thymoma, and uterine cancers. Half of the cancers (including all lung cancers) occurred in past/current smokers, versus one-third of patients without cancer. Multivariate analyses indicated that overall cancer risk was related primarily to male sex and older age at SLE diagnosis. In addition, smoking was associated with lung cancer. For breast cancer risk, age was positively associated and antimalarial drugs were negatively associated. Antimalarial drugs and higher disease activity were also negatively associated with nonmelanoma skin cancer risk, whereas age and cyclophosphamide were positively associated. Disease activity was associated positively with hematologic and negatively with nonmelanoma skin cancer risk. CONCLUSION: Smoking is a key modifiable risk factor, especially for lung cancer, in SLE. Immunosuppressive medications were not clearly associated with higher risk except for cyclophosphamide and nonmelanoma skin cancer. Antimalarials were negatively associated with breast cancer and nonmelanoma skin cancer risk. SLE activity was associated positively with hematologic cancer and negatively with nonmelanoma skin cancer. Since the absolute number of cancers was small, additional follow-up will help consolidate these findings.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".