Digital ulcers in systemic sclerosis: Prevention by treatment with bosentan, an oral endothelin receptor antagonist
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".