An international cohort study of cancer in systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: There is increasing evidence in support of an association between systemic lupus erythematosus (SLE) and malignancy, but in earlier studies the association could not be quantified precisely. The present study was undertaken to ascertain the incidence of cancer in SLE patients, compared with that in the general population. METHODS: We assembled a multisite (23 centers) international cohort of patients diagnosed as having SLE. Patients at each center were linked to regional tumor registries to determine cancer occurrence. Standardized incidence ratios (SIRs) were calculated as the ratio of observed to expected cancers. Cancers expected were determined by multiplying person-years in the cohort by the geographically matched age, sex, and calendar year-specific cancer rates, and summing over all person-years. RESULTS: The 9,547 patients from 23 centers were observed for a total of 76,948 patient-years, with an average followup of 8 years. Within the observation interval, 431 cancers occurred. The data confirmed an increased risk of cancer among patients with SLE. For all cancers combined, the SIR estimate was 1.15 (95% confidence interval [95% CI] 1.05-1.27), for all hematologic malignancies, it was 2.75 (95% CI 2.13-3.49), and for non-Hodgkin's lymphoma, it was 3.64 (95% CI 2.63-4.93). The data also suggested an increased risk of lung cancer (SIR 1.37; 95% CI 1.05-1.76), and hepatobiliary cancer (SIR 2.60; 95% CI 1.25, 4.78). CONCLUSION: These results support the notion of an association between SLE and cancer and more precisely define the risk of non-Hodgkin's lymphoma in SLE. It is not yet known whether this association is mediated by genetic factors or exogenous exposures.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".