Characteristics of Overlap Syndrome in a Large Cohort of Brazilian Patients With Systemic Sclerosis: A Retrospective Analysis
Bibliographic record
Abstract
Objective Systemic sclerosis (SSc) often presents alongside other connective tissue disorders, termed "overlap syndrome (OS)." This study aimed to characterize patients with SSc with OS compared to those without OS in a tertiary university hospital in Brazil. Methods A retrospective analysis of demographic, clinical, and laboratory data from 787 SSc patients was performed using electronic medical records. Patients were classified based on the 2013 American College of Rheumatology/European Alliance of Associations for Rheumatology criteria for SSc and additional criteria for systemic lupus erythematosus (SLE), idiopathic inflammatory myopathy (IIM), Sjögren disease (SjD), and rheumatoid arthritis (RA). Statistical analysis, including univariate and multivariate methods, identified factors associated with OS. Results Ninety-one patients (11.6%) had OS, mainly with SLE (29.7%), SjD (26.4%), RA (24.2%), or IIM (19.8%). Patients with OS were younger, with an earlier age at onset (P= 0.004) and at diagnosis (P= 0.003). They presented a higher prevalence of limited SSc (Ptrend= 0.06), musculoskeletal symptoms (P< 0.001), neoplasia (P= 0.03), and sicca symptoms (P< 0.001); and were associated with a lower frequency of pulmonary hypertension (P= 0.048) and comorbidities such as diabetes mellitus (P= 0.02) and dyslipidemia (P= 0.02). A higher prevalence of anti-Ro (P= 0.007) and a lower prevalence of anti-Scl70 (P= 0.003) were also observed. Patients with OS were more frequently prescribed glucocorticoids (GCs;P< 0.001), methotrexate (P= 0.01), and leflunomide (P= 0.001). Multivariate analysis identified limited SSc (odds ratio [OR] 3.1), neoplasia (OR 3.4), and use of GCs (OR 8.2) and leflunomide (OR 5.5) with OS. No worse prognosis was observed. Conclusion Overall, Brazilian patients with SSc with OS have distinct clinical characteristics but do not have a worse prognosis compared to those without OS.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".