Defining Disease Activity in Systemic Sclerosis
Bibliographic record
Abstract
Abstract Purpose of Review Systemic sclerosis (SSc) is a multisystem autoimmune disease characterised by the presence of fibrosis, microvasculopathy and inflammation. The complex pathogenesis and widespread organ involvement have made assessment and quantification of overall disease activity challenging. In this review, we present an update of the assessment of disease activity in SSc. Recent Findings There has been increasing interest in the use of composite outcome measures to assess the totality of SSc and measure multidimensional disease constructs such as activity and damage. Recently, the Scleroderma Clinical Trials Consortium (SCTC) published a new SSc Activity Index (SCTC-AI) to quantify disease activity across nine domains of disease. In this article, we discuss both the challenges of measuring disease activity in SSc and the rationale and clinical importance of accurate quantification of disease activity. Summary Heterogeneity in clinical presentation, variation in the tempo of disease and variable responsiveness to treatment at different disease stages has resulted in significant challenges in classification and assessment of SSc patients. However, two SSc-specific activity indices now exist to quantify states of high disease activity. Further work is required to establish whether composite outcome measures offer superior measures of treatment response in SSc clinical trials and what the role of the assessment of disease activity is in the recruitment and assessment of participants in trials of novel therapies.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".