Immunosuppressive Therapies for Diffuse Systemic Sclerosis: A Scoping Review
Bibliographic record
Abstract
Objectives Systemic sclerosis (SSc) is a progressive autoimmune disease characterized by significant multi-organ damage. Its intricate pathophysiology involves various immune pathways, including the activation of B-cells, which are thought to play a crucial role in disease progression by driving fibrosis and causing vascular damage.[1] Historically, treatment options were limited; however, a larger range of treatments targeting the immune system have been tested over the last decade, including CAR-T therapy,[2] a treatment modality that Ottawa is working toward in collaboration with the CLIC pan-Canadian CAR-T network.[3] We conducted a scoping review to summarize trends of immunosuppressive therapies used for SSc treatment, particularly relating to skin and lung involvement. Methods A scoping review was conducted in adherence to the PRISMA-ScR methodology. We searched 3 databases (MEDLINE, EMBASE, Cochrane Central) for studies published from January 2004 to January 2024. Titles and abstracts, then full-text articles were screened, focusing on skin and lung function outcomes in SSc. Results A total of 3891 abstracts were screened, resulting in the inclusion of 294 studies −149 abstracts and 145 full-text articles (Figure 1). Overall, 41 unique therapies studied were identified. The 10 most commonly studied treatments were: rituximab (n=84, 20.6%), cyclophosphamide (n=77, 18.9%), mycophenolate mofetil (n=52, 12.8%), autologous hematopoietic stem cell transplantation (autoHSCT) (n=29, 7.1%), tocilizumab (n=23, 5.7%), glucocorticoids (n=23, 5.7%), azathioprine (n=19, 4.7%), methotrexate (n=14, 3.4%), imatinib (n=14, 3.4%), and nintedanib (n=10, 2.5%). The peak number of studies pertaining to cyclophosphamide (28.9%) and mycophenolate mofetil (17.4%) treatments occurred in 2014-2018. However, from 2019-2024, studies of rituximab (27.2%) and autoHSCT (11.3%) became more prevalent. Figure 1. Immunosuppressive therapies for Systemic Sclerosis, with skin and/or lung involvement, studied between 2004–2024. Conclusion Over the past 2 decades, SSc treatment has progressed significantly, shifting between immunosuppressive agents and autoHSCT. These advancements highlight the evolving landscape of SSc management and emphasize the need for innovative approaches, including advanced cellular therapies such as CAR-T-based treatments. [1.] Thoreau B. Role of B-cell in the pathogenesis of systemic sclerosis. Frontiers in Immunology 2022;13:933468. [2.] Bergmann C. Annals of the Rheumatic Diseases 2023;82(8):1117-20. [3.] Kekre N. Frontiers in immunology 2022;13:1074740.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".