Venous Thromboembolism in Systemic Sclerosis: Prevalence, Risk Factors, and Effect on Survival
Bibliographic record
Abstract
OBJECTIVE: Whether systemic sclerosis (SSc) confers increased risk of venous thromboembolism (VTE) is uncertain. We evaluated the prevalence, risk factors, and effect of VTE on SSc survival. METHODS: A cohort study was conducted of subjects with SSc who fulfilled the American College of Rheumatology/European League Against Rheumatism classification criteria between 1970 and 2017. Deep vein thrombosis was defined as thrombus on extremity ultrasound. Pulmonary embolism was defined as thrombus on thorax computed tomography angiogram. Risk factors for VTE and time to all-cause mortality were evaluated. RESULTS: Of the 1181 subjects, 40 (3.4%) experienced VTE events. The cumulative incidence of VTE was 2.7 (95% CI 1.9-3.7) per 1000 patient-years. Pulmonary arterial hypertension (PAH; OR 3.77, 95% CI 1.83-8.17), peripheral arterial disease (OR 5.31, 95% CI 1.99-12.92), Scl-70 (OR 2.45, 95% CI 1.07-5.30), and anticardiolipin antibodies (OR 5.70, 95% CI 1.16-21.17) were predictors of VTE. There were 440 deaths. There was no difference in survival between those with and without VTE (HR 1.16, 95% CI 0.70-1.91). Interstitial lung disease (HR 1.54, 95% CI 1.27-1.88) and PAH (HR 1.35, 95% CI 1.10-1.65) were predictors of mortality. CONCLUSION: The risk of VTE in SSc is comparable to the general population. The presence of PAH, peripheral arterial disease, Scl-70, and anticardiolipin antibodies are risk factors for VTE. VTE does not independently predict SSc survival.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".