Meta‐Analysis of Healing and Prevention of Digital Ulcers in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To assess the efficacy of therapies in healing and preventing digital ulcers (DUs) in systemic sclerosis (SSc; scleroderma). METHODS: Medline and EMBASE databases, and American College of Rheumatology and European League Against Rheumatism abstracts, were searched. Randomized controlled trials (RCTs) with outcomes investigating healing or prevention of DUs in SSc and comparing a pharmacologic therapy with placebo or an active agent were included. The pooled risk ratios (RRs) using the fixed-effects model were calculated and heterogeneity was tested using the I(2) statistic. RESULTS: Sixty studies were found; 19 were not randomized, and 10 did not give DU quantitative data or no comparison of a different drug, leaving 31 RCTs with a total of 1,989 patients. Quality was 3 of 5 or less for 11 trials. DUs were not the primary outcome in many RCTs. Phosphodiesterase type 5 (PDE-5) inhibitors were significant for DU healing (RR 3.28 [95% confidence interval (95% CI) 1.32, 8.13], P = 0.01). Two large bosentan trials were significant for mean number of new DUs (standardized mean difference [SMD] -0.34 [95% CI -0.57, -0.11], P = 0.004). Oral prostacyclins were not statistically different from placebo, but intravenous (IV) iloprost prevented new DUs (SMD 0.77 [95% CI -1.46, -0.08], P = 0.03). Single trials for atorvastatin and vitamin E were positive in the prevention and healing of DU, respectively. There were many negative trials: antiplatelet therapy, oral N-acetylcysteine, heparin, dimethyl sulfoxide, ketanserin, prazosin, prostaglandin E1, cyclofenil, quinapril, and topical nitroglycerin formulation. CONCLUSION: Small sample sizes, few comparative trials, and heterogeneity limits the conclusions. The results suggest a role for PDE-5 inhibitors in the healing of DUs; bosentan and IV iloprost may prevent new DUs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".