The Recurrence of Digital Ulcers in Patients with Systemic Sclerosis after Discontinuation of Oral Treprostinil
Bibliographic record
Abstract
OBJECTIVE: Prior studies investigating the efficacy of oral treprostinil to treat digital ulcers (DU) in systemic sclerosis (SSc)-associated Raynaud phenomenon have yielded conflicting results. In this investigation, we examined whether DU burden increased after patients withdrew from oral treprostinil that was administered during an open-label extension study. METHODS: A multicenter, retrospective study was conducted to determine DU burden in the year after withdrawal from oral treprostinil. DU burden 3-6 months (Time A) and > 6-12 months (Time B) after drug withdrawal was compared with DU burden at baseline, defined as the last day receiving drug in the open-label extension study, by a paired Student t test. Changes in DU burden while receiving drug in the open-label study were compared with changes in DU burden at Time B by a paired Student t test. RESULTS: Fifty-one patients from 9 clinical sites were included for analysis. DU burden increased significantly from baseline (mean 0.47) to Time A (mean 2.1, p = 0.002, n = 23) and Time B (mean 1.45, p = 0.013, n = 30). Total DU burden decreased during oral treprostinil exposure (mean change -0.6) and then increased by Time B (mean change 1.05, p = 0.0027 for comparison, n = 30). In the year after drug withdrawal, many patients required vasodilator therapy and pain medications. Three patients were hospitalized for complications from DU, and 4 patients required surgery for DU. CONCLUSION: Total DU burden increased significantly after discontinuation of oral treprostinil. These data provide supportive evidence of a beneficial effect of oral treprostinil for the vascular complications of SSc and suggest that further study is warranted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".