Infections Are Not Increased in Scleroderma Compared to Non-Inflammatory Musculoskeletal Disorders Prior to Disease Onset
Bibliographic record
Abstract
UNLABELLED: The etiology of scleroderma (SSc) is unknown; immunogenic stimuli such as infections and vaccinations could theoretically be risk factors for scleroderma. Our objective was to assess the relationship between viral and bacterial infections, and vaccinations, prior to diagnosis of SSc compared to non-inflammatory controls. METHODS: A questionnaire was sent to individuals with SSc (n =83) and controls (n=351) with non-inflammatory musculoskeletal (MSK) disorders (osteoarthritis, n = 204; tendonitis, n = 58; fibromyalgia, n= 89) from a rheumatology practice. Questions ascertained past infections, exposure to infectious agents and vaccination history. RESULTS: The response rate was 78% (SSc) and 56% (MSK controls). The mean age was 56 +/- 1.6 (SSc) and 58 +/- 0.9 (MSK); 88% (SSc) and 82% (MSK) were female. No association between prior infections and SSc was observed. In fact, controls were more likely than SSc subjects to report any infection within 1-year prior to disease diagnosis (35% vs. 16%, p<0.006), or to have suffered a trauma to affected joints prior to diagnosis (44% vs. 19%, p<0.0002). Within the 1-year prior to disease diagnosis, controls reported slightly more streptococcal infections (p<0.2), infections with diarrhea and vomiting (p<0.3), and antibiotic use (p<0.09), although none of these results were statistically significant. Histories of any hepatitis, rubella, any bacterial infection, and having had a previous positive tuberculosis skin test were not significantly different between groups and were actually more often reported by the control subjects. SSc reported slightly more hepatitis B (p<0.08), more rheumatic fever (p<0.8) in past, and herpes zoster (p<0.4), although no differences reached significance. CONCLUSION: This study does not support that self-report of symptomatic infections are more likely to occur ever (prior to diagnosis) or within 1-year prior to symptom onset of SSc, or that vaccinations in adulthood trigger 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".