Is There Relevance of a Positive Rheumatoid Factor in Systemic Sclerosis? A Systematic Review of the Literature
Bibliographic record
Abstract
Objectives The purpose of this review was to determine the relevance of a positive rheumatoid factor (RF) in Systemic sclerosis (SSc). SSc is a rare autoimmune disease characterized by vasculopathy, fibrosis and autoantibodies. As high as 30% of those with SSc are rheumatoid factor (RF) positive, however the relationship between RF and various manifestations of SSc are not fully described. Methods A literature search was performed on PubMed, Embase, CINAHL, and Cochrane using the following search terms: systemic sclerosis, rheumatoid factor, and scleroderma. Articles associated with RF in SSc were reviewed. Results Rheumatoid arthritis prevalence is slightly increased in SSc (3%). However, if there is not an overlap with RA, RF does not seem to predict joint manifestations, arthritis or arthralgia in SSc. In addition to SSc-RA overlap syndrome, RF may be a predictor for SSc overlapping with Sjögren’s disease (SSc-SS). It is uncertain as to whether RF is protective for SSc-ILD. RF does not seem to predictive of other well-known serological markers of SSc, such as anti-topoisomerase antibodies, anti-centromere antibodies or Ro52/Trim21, however RF may be associated with elevated ESR or CRP levels. In terms of skin manifestations, individuals with elevated IgA-RF have increased the likelihood of having a dcSSC subtype, telangiectasias and digital pitting scars. Evidence is lacking in SSc and RF with respect to other organ manifestations, such as heart and kidney. Conclusion Further examination is warranted to understand the role of RF in SSc.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".