Treatment recommendations for the systemic pharmacological treatment of systemic sclerosis digital ulcers: Results from the World Scleroderma Foundation Ad Hoc Committee
Bibliographic record
Abstract
Introduction: Digital ulcers are an important disease manifestation of systemic sclerosis and are associated with significant morbidity. As such, there is an urgent need for the development of evidence-based recommendations to guide clinicians in the treatment of digital ulcers. Methods: A steering committee of international experts was established. A systematic review of the literature pertaining to the use of pharmacologic treatments in the management of digital ulcers was performed to inform the development of treatment recommendations for systemic sclerosis digital ulcers. Consensus methodology was used to develop the final treatment recommendations. Results: The World Scleroderma Foundation committee agreed on 8 overarching treatment principles and 10 pharmacologic treatment recommendations for the management of systemic sclerosis digital ulcers. Phosphodiesterase 5 inhibitors and intravenous iloprost were recommended for the management of acute digital ulcers. Bosentan was recommended for prevention of digital ulcers. Conclusion: This study has yielded pragmatic treatment recommendations to direct treatment decisions for the management of systemic sclerosis digital ulcers. In addition, results have highlighted areas in need of future research in order to improve patient outcomes.
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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 | 0.099 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".