Digital Ulcers in Ssc Treated with Oral Treprostinil: A Randomized, Double-Blind, Placebo-Controlled Study with Open-Label Follow-up
Bibliographic record
Abstract
Background Prostacyclins are routinely used to treat vascular features of systemic sclerosis (SSc, scleroderma) but require parenteral infusion or inhalation. This study evaluated the safety and efficacy of oral treprostinil in digital ulcers secondary to SSc. Methods This was a randomized (1:1) placebo-controlled, multicenter study in adults with SSc and at least one active digital ulcer at entry. Oral treprostinil was administered twice daily and titrated to maximum tolerated dose with clinical assessments at Weeks 5, 10, 15 and 20. The primary endpoint was change in net digital ulcer burden. Secondary outcomes included ulcer healing and prevention, measures of hand function, quality of life, Raynaud phenomenon and global assessments. Simplified data were gathered during open-label follow up. Results Enrolled were 147 patients (109F/38M), mean age 48.8 years with SSc of mean duration 10.5 years. At week 20, mean net ulcer burden was reduced −0.43 ulcers on treprostinil (1.80 vs. 1.37) and −0.10 ulcers on placebo (1.61 vs. 1.51; p = 0.20). There were no effects on ulcer healing or prevention, and small, inconsistent effects on Raynaud phenomenon, global assessment, hand function and quality-of-life measures. In open-label follow-up, there was a continued, small reduction in net ulcer burden (-0.52 month 3, n = 104; −0.64 month 12, n = 36). Common adverse effects were headache, nausea, diarrhea, jaw pain, flushing and other gastrointestinal symptoms. Conclusions Administration of oral treprostinil twice daily over 20 weeks was associated with small and statistically insignificant reduction in net ulcer burden in comparison to placebo.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".