Preliminary Clinical and Laser Speckle Contrast Analysis Data on Selexipag Efficacy for the Treatment of Digital Vasculopathy in Systemic Sclerosis
Bibliographic record
Abstract
Objective Systemic sclerosis (SSc) is burdened by Raynaud phenomenon (RP) and digital ulcers (DUs), and sometimes standard vasoactive therapies are ineffective or contraindicated. Selexipag is an oral selective IP prostacyclin receptor agonist approved for the treatment of SSc-related pulmonary arterial hypertension. We aimed to evaluate the clinical and instrumental efficacy of selexipag in SSc digital vasculopathy. Methods Patients with SSc with severe digital vasculopathy refractory or with contraindication to all other vasoactive therapies were administered selexipag. RP- and DU-related clinical outcomes were evaluated, and digital perfusion was assessed by laser speckle contrast analysis (LASCA), all at baseline and after 3 months. Results Selexipag was administered to 9 patients with SSc (66.6% female, mean age 52.3 [SD 16.6] yrs). One patient had to stop the drug because of adverse effects. After 3 months of selexipag administration, there was a significant reduction in RP daily episodes (P= 0.01) and RP mean duration (P= 0.04). The number of DUs decreased from 10 to 4 without reaching statistical significance. A significant improvement in mean perfusion of the fingers (P= 0.02) was observed with LASCA. Conclusion Selexipag showed good potential for the treatment of SSc digital vasculopathy. Our results are certainly preliminary, yet quite encouraging. New trials for the evaluation of selexipag efficacy in SSc digital vasculopathy are needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".