Performance of the 2013 American College of Rheumatology/European League Against Rheumatism Classification Criteria for Systemic Sclerosis (SSc) in Large, Well-defined Cohorts of SSc and Mixed Connective Tissue Disease
Bibliographic record
Abstract
OBJECTIVE: To assess the 2013 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) Classification Criteria for Systemic Sclerosis (SSc) on defined subgroups of SSc and in mixed connective tissue disease (MCTD) as an SSc-related disease. METHODS: The 2013 ACR/EULAR criteria were assessed in 425 consecutive patients suspected to have SSc and seen at Oslo University Hospital, and in the nationwide Norwegian MCTD cohort (n = 178). In the SSc group, 239/425 patients had disease duration < 3 years (in 82 of these, duration was < 1 yr). Patients were subgrouped as limited SSc (n = 294), diffuse SSc (n = 97), SSc sine scleroderma (n = 10), and early SSc (prescleroderma; n = 24). Item data were complete, except nailfold capillaroscopy and telangiectasia results, missing in the MCTD cohort. RESULTS: The 2013 ACR/EULAR SSc criteria were met by 409/425 patients (96%) in the SSc group. For comparison, only 75% (293/391) met the 1980 ACR SSc classification criteria. All the novel items in the 2013 ACR/EULAR criteria were frequent in the SSc cohort. Considering that there were missing data on 2 items, 10% (18/178) of the MCTD cohort met the 2013 ACR/EULAR criteria, giving an estimated specificity of 90% toward this SSc-like disorder. CONCLUSION: In our large and representative group of consecutive patients with SSc, the 2013 ACR/EULAR SSc criteria were more sensitive than the ACR 1980 criteria. However, the new criteria did not completely segregate SSc from MCTD, making specificity a potential issue.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".