A Delphi exercise and cluster analysis to aid in the development of potential classification criteria for systemic sclerosis using SSc experts and databases.
Bibliographic record
Abstract
OBJECTIVES: Since the 1980 ACR classification criteria for systemic sclerosis (SSc) do not identify 20% with SSc, revised criteria are necessary. METHODS: Suggested new criteria from the literature were sent in random order to 96 SSc experts. A 3-round Delphi Consensus eliminated criteria. Then cluster analysis reduced items. The Canadian Scleroderma Research Group (CSRG) database was used to determine the prevalence of each item. RESULTS: Seventy-one of 96 (71%) completed all 3 rounds; 47 items were expanded to 76 in round 2. Thirty items had at least 50% consensus and 18 had >75% agreement to include (a priori cut point). Clustering occurred for 4 categories: proximal to MCP skin involvement, vascular abnormalities, autoantibodies and tissue damage. Proximal to MCPs skin involvement identified 80% of patients. Adding one item from each of the other 3 categories or 1 or more items from 2 of 3 remaining categories increased the proportion of patients classified to 94% in CSRG patients. Categories included (1) Vascular (dilated capillaries, telangiectasia, Raynaud's phenomenon [RP]), (2) Autoantibodies (anticentromere [ACA] or antitopoisomeraseI [Topo1]) and (3) Fibrosis/damage (esophogeal dysmotility dysphagia, sclerodactyly, digital ulcers). In the CSRG, 98% were identified if using proximal skin involvement; or sclerodactyly plus one of: RP, ACA or Topo1. CONCLUSIONS: This is a first step toward developing new SSc classification criteria. A Delphi exercise alone cannot suffice for item reduction. Also, validation prospectively in SSc patients and diseases that mimic SSc is needed in order to calculate sensitivity and specificity of future criteria.
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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 | 0.158 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".