Progress in the Evolution of Systemic Sclerosis Classification Criteria and Recommendation for Additional Comparative Specificity Studies
Bibliographic record
Abstract
Sensitivity and specificity performance of the 2013 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) classification criteria for systemic sclerosis (SSc)1,2 were assessed by Hoffmann-Vold, et al in this issue of The Journal 3. Sensitivity was tested in 425 consecutive, representative patients with SSc seen at the Oslo University Hospital and compared to the original 1980 American Rheumatism Association (now ACR) preliminary criteria for definite SSc4 and the subsequent 2001 criteria for early SSc proposed by LeRoy and Medsger5. Sensitivity (also called the true positive percent) is a binary classification measure of performance function, which indicates the proportion of all true cases in the study cohort who satisfy criteria. The 425 SSc cases analyzed in the Norwegian study satisfied either the 1980 ACR criteria for certain diagnosis or the 2001 LeRoy and Medsger criteria for early disease3. Specificity of the 2013 criteria was tested in a Norwegian nationwide cohort of 178 patients with mixed connective tissue disease (MCTD). Specificity (also called the true negative percent) indicates the proportion of the comparative or control subjects, like those having MCTD, who do not satisfy a criteria set. SSc is a heterogeneous, multisystem, and multistage disorder marked by variable expression in its patterns of presentation and course of disease. Its pathogenesis is believed to differ from Raynaud disease and several of the other systemic connective tissue diseases (CTD), as do systemic lupus erythematosus (SLE) and idiopathic inflammatory myopathy (IIM). However, MCTD has frequent overlapping clinical features with SSc6,7. A vexing classification issue for SSc criteria development is how to deal with MCTD patients whose diagnosis depends heavily upon the presence of high titer antibody to U1RNP rather than a typical clinical profile7. Analytical complexity can arise in comparing … Address correspondence to Dr. Masi; E-mail: amasi{at}uic.edu
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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 | 0.348 | 0.431 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.012 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".