Epidemiology of Cancer in Systemic Sclerosis—Systematic Review and Meta-Analysis of Cancer Incidence, Predictors and Mortality*
Bibliographic record
Abstract
Objectives: The study was conducted to improve our understanding of the epidemiology of cancer in systemic sclerosis (SSc) by evaluating the incidence, prevalence, relative risk of overall and site-specific malignancies, predictors and cancer-attributable mortality. Methods: MEDLINE, CINAHL, EMBASE and Cochrane Library (inception-May 2012) were searched. Estimates were combined using a random effects model. Consistency was evaluated using the I2 statistic. Results: 4876 citations were searched to identify 60 articles. The average incidence of malignancy in SSc was 14 cases/1000 person-years; the prevalence ranged between 4%-22%. Cancer was the leading cause of non-SSc related deaths with a mean of 38%. Overall SIR for all-site malignancy risk was 1.85 (95%CI 1.52, 2.25; I276%). There was a greater risk of lung (SIR 4.69, 95%CI 2.84, 7.75; I293%) and haematological (SIR 2.58, CI 95% 1.75, 3.81; I20%) malignancies, including non-Hodgkin’s lymphoma (SIR 2.55, 95%CI 1.40, 4.67; I20%). SSc patients were at a higher risk of leukemia (SIR 2.79, 95%CI 1.22, 6.37; I20%), malignant melanoma (SIR 2.92, 95%CI 1.76, 4.83; I235%), liver (SIR 4.75, 95%CI 3.09, 7.31; I20%), cervical (SIR 2.28, 95%CI 1.26, 4.09; I254%) and oropharyngeal (SIR 5.0, 95%CI 2.18, 11.47; I258%) cancers. Risk factors include a-RNAP I/III seropositivity, male sex, and late onset SSc. Smoking and longstanding interstitial lung disease increase the risk of lung cancer; Barrett’s esophagus and a positive family history of breast cancer, respectively, increase the risk of esophageal adenocarcinoma and breast cancer. Conclusions: SSc patients have a two-fold increase in all-site malignancy, and greater risk of lung and haematological malignancies that contribute significantly to mortality. Vigilance should be considered in SSc patients with risk factors for cancer.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".