Prevalence, Correlates and Outcomes of Gastric Antral Vascular Ectasia in Systemic Sclerosis: A EUSTAR Case-control Study
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence, determine the subgroups at risk, and the outcomes of patients with systemic sclerosis (SSc) and gastric antral vascular ectasia (GAVE). METHODS: We queried the European League Against Rheumatism Scleroderma Trials and Research (EUSTAR) network for the recruitment of patients with SSc-GAVE. Each case was matched for cutaneous subset and disease duration with 2 controls with SSc recruited from the same center, evaluated at the time the index case made the diagnosis of GAVE. SSc characteristics were recorded at the time GAVE occurred and the last observation was collected to define the outcomes. RESULTS: Forty-nine patients with SSc and GAVE were included (24 with diffuse cutaneous SSc) and compared to 93 controls with SSc. The prevalence of GAVE was estimated at about 1% of patients with SSc. By multivariate analysis, patients with SSc-GAVE more frequently exhibited a diminished (< 75%) DLCO value (OR 12.8; 95% CI 1.9-82.8) despite less frequent pulmonary fibrosis (OR 0.2; 95% CI 0.1-0.6). GAVE was also associated with the presence of anti-RNA-polymerase III antibodies (OR 4.6; 95% CI 1.2-21.1). SSc-GAVE was associated with anemia (82%) requiring blood transfusion (45%). Therapeutic endoscopic procedures were performed in 45% of patients with GAVE. After a median followup of 30 months (range 1-113 months), survival was similar in patients with SSc-GAVE compared to controls, but a higher number of scleroderma renal crisis cases occurred (12% vs 2%; p = 0.01). CONCLUSION: GAVE is rare and associated with a vascular phenotype, including anti-RNA-polymerase III antibodies, and a high risk of renal crisis. Anemia, usually requiring blood transfusions, is a common complication.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".