Risk of malignancy in patients with systemic lupus erythematosus: Systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Malignancy is a potential comorbidity in patients with systemic lupus erythematosus (SLE). However, risk by malignancy type remains to be fully elucidated. We evaluated the risk of malignancy type in SLE patients in a systematic review and meta-analysis. METHODS: test. FINDINGS: Forty-one studies reporting on 40 malignancies (one overall, 39 site-specific) were included in the meta-analysis. The pooled RR for all malignancies from 3694 events across 80 833 patients was 1.18 (95% CI: 1.00-1.38). The risk of 24 site-specific malignancies (62%) was increased in SLE patients. For malignancies with ≥6 studies, non-Hodgkin lymphoma and Hodgkin lymphoma risk was increased >3-fold; myeloma and liver >2-fold; cervical, lung, bladder, and thyroid ≥1.5-fold; stomach and brain >1.3-fold. The risk of four malignancies (breast, uterine, melanoma, prostate) was decreased, whereas risk of 11 other malignancies did not differ between SLE patients and the general population. Heterogeneity ranged between 0% and 96%, and 63% were non-significant. INTERPRETATION: The risk of overall and some site-specific malignancies is increased in SLE compared with the general population. However, the risk for some site-specific malignancies is decreased or did not differ. Further examination of risk profiles and SLE patient phenotypes may support guidelines aimed at reducing malignancy risk. FUNDING: AstraZeneca. SYSTEMATIC REVIEW REGISTRATION: PROSPERO number: CRD42018110433.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".