Systemic pharmacological treatment of digital ulcers in systemic sclerosis: a systematic literature review
Bibliographic record
Abstract
OBJECTIVE: To evaluate the evidence concerning systemic pharmacological treatments for SSc digital ulcers (DUs) to inform the development of evidence-based treatment guidelines. METHODS: A systematic literature review of seven databases was performed to identify all original research studies of adult patients with SSc DUs. Randomized controlled trials (RCTs) and prospective longitudinal observational studies (OBSs) were eligible for inclusion. Data were extracted, applying the patient, intervention, comparison, outcome framework, and risk of bias (RoB) was assessed. Due to study heterogeneity, narrative summaries were used to present data. RESULTS: Forty-seven studies that evaluated the treatment efficacy or safety of pharmacological therapies were identified among 4250 references. Data from 18 RCTs of 1927 patients and 29 OBSs of 661 patients, at various RoB (total 2588 patients) showed that i.v. iloprost, phosphodiesterase-5 inhibitors and atorvastatin are effective for the treatment of active DUs. Bosentan reduced the rate of future DUs in two RCTs (moderate RoB) and eight OBSs at low to high RoB. Two small studies (moderate RoB) indicate that Janus kinase inhibitors may be effective for the treatment of active DUs, otherwise there are no data to support the use of immunosuppression or anti-platelet agents in the management of DUs. CONCLUSION: There are several systemic treatments, across four medication classes, that are effective therapies for the management of SSc DUs. However, a lack of robust data means it is not possible to define the optimal treatment regimen for SSc DUs. The relatively low quality of evidence available has highlighted further areas of research need.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".