Gastric Antral Vascular Ectasia in Systemic Sclerosis: Demographics and Disease Predictors
Bibliographic record
Abstract
OBJECTIVES: To evaluate patients with systemic sclerosis (SSc) who have gastric antral vascular ectasia (GAVE), to further characterize this disease association, and to identify factors that may predict which patients with SSc are at greatest risk for the development of GAVE. METHODS: Patients with a diagnosis of both SSc and GAVE were identified from the Division of Rheumatology at Georgetown University and Thomas Jefferson University. A chart review was conducted to obtain the demographic data. RESULTS: Twenty-eight patients were included in this analysis, including 17 with diffuse cutaneous (dcSSc) and 11 with limited cutaneous SSc (lcSSc). The mean disease duration at diagnosis with GAVE was 21.5 months for dcSSc and 84.3 months for lcSSc (p = 0.025). Seventy-six percent of patients with dcSSc developed GAVE within 18 months of first scleroderma symptom onset. Over half of patients with early GAVE also had rapidly progressive cutaneous disease. Only 4% had antitopoisomerase I antibody. Although only 1 patient was tested and had positive RNA polymerase (RNAP) III, RNAP III may be overrepresented in this GAVE population. Mean hematocrit levels were 23.8% in dcSSc and 29% in lcSSc. CONCLUSION: dcSSc is associated with earlier development of GAVE, as well as more severe anemia requiring more therapeutic interventions. Rapid progression of cutaneous disease may suggest earlier development of GAVE. Absence of antitopoisomerase I antibodies and presence of antibodies to RNAP III/speckled antinuclear antibody pattern may be useful to identify the subset of patients with SSc with increased risk for GAVE.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".