Recommendations for the local management of digital ulcers in systemic sclerosis: A report from the World Scleroderma Foundation (WSF) ‘Ad hoc committee’
Bibliographic record
Abstract
Introduction: Digital ulcers (DUs) stand out as one of the most prevalent and clinically meaningful manifestations of systemic sclerosis (SSc) and are associated with significant morbidity. While systemic (pharmacological) therapy is currently established as the 'standard of care', effective local ulcer management remains crucial for all cases of DUs. This is particularly true for patients who cannot tolerate systemic treatments or in the case of refractory SSc-DUs. On this background, there is a pressing demand for the formulation of evidence-based guidelines to assist clinicians and patients in navigating the local treatment options for DUs. Methods: A steering committee of international experts was established by the World Scleorderma Foundation (WSF) Digital Ulcer (DU) ad hoc committee. Two systematic literature reviews on local non-surgical and surgical treatments for the management of SSc-DUs were performed to inform the development of local treatment recommendations for SSc-DUs. Consensus methodology was used to develop the final treatment recommendations. Results: Six overarching treatment principles and eight local treatment recommendations (five non-surgical and three surgical) were agreed upon for the management of SSc-DU. Among topical non-surgical options, botulin toxin can be conditionally recommended for refractory and/or severe DUs. Among surgical treatments, autologous adipose tissue grafting might be recommended for DU healing when combined with background systemic treatments. Conclusion: These recommendations are specifically tailored to guide treatment decisions concerning both local and non-pharmacological approaches to managing SSc-related DUs. Our work has highlighted a notable quality gap in comparison to systemic treatments, underscoring the scarcity of high-quality studies concerning this topic.
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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 | 0.095 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".