Management of Raynaud’s phenomenon in systemic sclerosis—a practical approach
Bibliographic record
Abstract
Raynaud's phenomenon is nearly universal in systemic sclerosis. Vasculopathy is part of systemic sclerosis. Raynaud's phenomenon can cause of complications and impairment, especially when tissue ischemia and digital ulcers develop. There are many treatment options for Raynaud's phenomenon in systemic sclerosis often with sparse data and few robust studies comparing the different treatment options. Recommendations from guidelines usually include calcium channel blockers as first-line pharmacological treatment. In the clinical setting, multiple variables such as financial factors, geography where access to medications varies, and patient factors, baseline hypotension, can influence the treatment for Raynaud's phenomenon and digital ulcers. Prostacyclins and PDE-5 inhibitors are reserved for more severe Raynaud's phenomenon or healing of digital ulcers. Prevention of digital ulcers may also include endothelin receptor blocker (bosentan) in some countries. Other treatments had less consensus. Algorithms developed by systemic sclerosis experts might be helpful in deciding which treatment to choose for each setting, using a step-wise strategy, which intends to complement guidelines. This review focuses on a practical approach to the treatment of Raynaud's phenomenon and digital ulcers in systemic sclerosis, based on algorithms designed by systemic sclerosis experts using consensus, and we review the evidence that supports treatment from initial to second and third-line options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".