Bosentan for Digital Ulcers in Patients with Systemic Sclerosis: A Prospective 3-year Followup Study
Bibliographic record
Abstract
To the Editor: Systemic sclerosis (SSc) is a complex autoimmune connective tissue disease characterized by cutaneous and visceral fibrosis and widespread vascular pathology1. Digital ulcers (DU) are a major clinical problem in SSc, occurring in about one-third of patients2. DU cause local pain and functional impairment and have a negative effect on quality of life for patients with SSc2. Therapeutic agents potentially used for management of DU include calcium channel blockers, α-adrenergic inhibitors, angiotensin II-converting enzyme inhibitors, prostacyclin analogs, phosphodiesterase-5 inhibitor, and others2. Bosentan is a specific orally active dual endothelin receptor antagonist that has been used for the treatment of pulmonary hypertension (PH) and recently for DU3–5. The objective of our study was to examine the effectiveness and safety of bosentan for healing DU in patients with SSc over the long term. For a cohort of 110 patients with SSc, 30 patients with DU were identified. All patients fulfilled the American College of Rheumatology criteria for SSc6 and all were refractory to calcium channel antagonists, angiotensin II inhibitors, or sildenafil. … Address reprint requests to Prof. Drosos. E-mail: adrosos{at}cc.uoi.gr
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".