Cancer Risk Factors in SLE: Multivariate Regression Analysis in 16,409 Patients
Bibliographic record
Abstract
Background : We assessed factors associated with cancer risk in systemic lupus erythematosus (SLE), relative to the general population, using a large international multi-centre clinical cohort (30 centres, 16,409 patients). Methods : Cancers were ascertained by registry linkage. We used Poisson hierarchical regression to assess for potential independent effects of sex, race/ethnicity, age group, SLE duration, and calendar-year period on the standardized incidence ratios (SIR; ratio of cancers observed to expected). The hierarchical model allowed for differences in effects across countries. The primary regression analyses were done using the overall cancer SIRs; in secondary analyses we focused on hematological cancer SIRs. Results : In adjusted analyses, we demonstrated lower SIR estimates for overall cancer risk, in black versus white SLE patients, in SLE patients of older versus younger age, and for patients with SLE duration of 5 years or more (versus lower duration). Female sex and calendar year were not clearly associated. Regarding hematological cancers specifically, SLE duration of 5 years or more again appeared to be associated with lower SIR estimates. Conclusion : Cancer risk in SLE is increased relative to the general population; this is particularly true for patients of white race/ethnicity, younger age, and of shorter SLE duration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".