The Belgian Systemic Sclerosis Cohort: Correlations Between Disease Severity Scores, Cutaneous Subsets, and Autoantibody Profile
Bibliographic record
Abstract
OBJECTIVE: To report baseline and followup data on the first 438 patients with systemic sclerosis (SSc) included in the Belgian Systemic Sclerosis Cohort. METHODS: According to LeRoy and Medsger's classification, 73 patients with limited SSc (lSSc), 279 with limited cutaneous SSc (lcSSc), and 86 with diffuse cutaneous SSc (dcSSc) were included. History was collected and clinical examination, blood tests, and paraclinical investigations were repeated. The Disease Activity Score (DAS) and Disease Severity Score (DSS) of several organ systems were computed. An organ system was considered to demonstrate SSc if the corresponding DSS was ≥ 1. RESULTS: At baseline, patients with dcSSc had more general, joint/tendon, muscle, gastrointestinal, and kidney involvement. Mean DLCO was below normal in patients with lSSc, indicating unsuspected lung involvement. Patients with anti-Scl-70 had more vascular, skin, joint/tendon, and lung involvement. Patients with anti-RNA polymerase III had more skin and joint/tendon involvement compared to patients with anticentromere. Time to death was statistically shorter for patients with dcSSc. New-onset lung disease was the most common complication over time. No changes in DAS were observed. By contrast, the general and the skin DSS worsened in patients with lcSSc and lSSc, respectively. Fifteen percent of patients with lSSc shifted to lcSSc at Month 30, but neither serology nor capillaroscopy findings at baseline were helpful in identifying those at risk. CONCLUSION: Our data indicate that the DSS can be used to define organ involvement in SSc. Differences can be seen between subsets classified not only according to cutaneous subtypes but also to autoantibody profile.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".