Progress in understanding the diagnostic and pathogenic role of autoantibodies associated with systemic sclerosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: At the time of diagnosis, systemic sclerosis (SSc) is often well established with significant irreversible tissue and organ damage. Definitions of 'early SSc' have been proposed, which include the presence of SSc-associated autoantibodies. In addition, functional autoantibodies that are believed to be involved in SSc pathogenesis need to be considered. In this review, recent advances in the diagnostic utility and pathogenic role of autoantibodies in early SSc are summarized. Moreover, we propose a clinical care pathway illustrating how autoantibody testing along with key clinical features can be used to make an earlier diagnosis of SSc. RECENT FINDINGS: Recent evidence has helped to develop a clearer understanding of the natural history, early clinical features, and autoantibodies that are predictors of SSc. The role of functional autoantibodies is leading to innovative approaches to evidence-based interventions and therapies that are based on mechanisms of disease. SUMMARY: Despite substantial advances, the high morbidity and mortality that currently characterizes SSc can largely be attributed to a delay in diagnosis, gaps in our understanding of the role of autoantibodies in early disease, and limited effective therapeutic options. An early and accurate diagnosis of SSc and use of autoantibody testing embedded in evidence-based clinical care pathways will help improve SSc-associated clinical outcomes and healthcare expenditures.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".