Digital ulcers in systemic sclerosis: Prevention by treatment with bosentan, an oral endothelin receptor antagonist
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Recurrent digital ulcers are a manifestation of vascular disease in patients with systemic sclerosis (SSc; scleroderma) and lead to pain, impaired function, and tissue loss. We investigated whether treatment with the endothelin receptor antagonist, bosentan, decreased the development of new digital ulcers in patients with SSc. METHODS: This was a randomized, prospective, placebo-controlled, double-blind study of 122 patients at 17 centers in Europe and North America, evaluating the effect of treatment on prevention of digital ulcers. The primary outcome variable was the number of new digital ulcers developing during the 16-week study period. Secondary assessments included healing of existing digital ulcers and evaluation of hand function using the Scleroderma Health Assessment Questionnaire. RESULTS: Patients receiving bosentan had a 48% reduction in the mean number of new ulcers during the treatment period (1.4 versus 2.7 new ulcers; P = 0.0083). Patients who had digital ulcers at the time of entry in the study were at higher risk for the development of new ulcers; in this subgroup the mean number of new ulcers was reduced from 3.6 to 1.8 (P = 0.0075). In patients receiving bosentan, a statistically significant improvement in hand function was observed. There was no difference between treatment groups in the healing of existing ulcers. Serum transaminase levels were elevated to >3-fold the upper limit of normal in bosentan-treated patients; this elevation is comparable with that observed in previous studies of this agent. Other side effects were similar in the 2 treatment groups. CONCLUSION: Endothelins may play an important role in the pathogenesis of vascular disease in patients with SSc. Treatment with the endothelin receptor antagonist bosentan may be effective in preventing new digital ulcers and improving hand function in patients with SSc.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it