Vasoactive Therapy in Systemic Sclerosis: Real-life Therapeutic Practice in More Than 3000 Patients
Bibliographic record
Abstract
OBJECTIVE: Vasculopathy is a key factor in the pathophysiology of systemic sclerosis (SSc) and the main cause for Raynaud phenomenon (RP), digital ulcers (DU), and/or pulmonary arterial hypertension (PAH). It is so far unknown how patients with SSc are treated with vasoactive agents in daily practice. To determine to which extent patients with SSc were treated with different vasoactive agents, we used data from the German Network for Systemic Scleroderma registry. METHODS: The data of 3248 patients with SSc were analyzed. RESULTS: Patients were treated with vasoactive drugs in 61.1% of cases (1984/3248). Of these, 47.6% received calcium channel inhibitors, followed by 34.2% treated with angiotensin-converting enzyme (ACE) inhibitors, 21.1% treated with intravenous (IV) prostanoids, 10.1% with pentoxifylline, 8.8% with angiotensin 1 receptor antagonists (AT1RA), 8.7% with endothelin 1 receptor antagonists (ET1RA), 4.1% with phosphodiesterase type 5 (PDE5) inhibitors, and 5.3% with others. Patients with RP received vasoactive therapy in 63.3% of cases, with DU in 70.1%, and with PAH in 78.2% of cases. Logistic regression analysis revealed that patients with PAH were significantly more often treated with PDE5 inhibitors and ET1RA, and those with DU with ET1RA and IV prostanoids. In addition, 41.8% of patients were treated with ACE inhibitors and/or AT1RA. Patients registered after 2009 received significantly more often ET1RA, AT1RA, and IV prostanoids compared with patients registered prior to 2005. CONCLUSION: These data clearly indicate that many patients with SSc do not yet receive sufficient vasoactive therapy. Further, in recent years, a marked change of treatment regimens can be observed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".