UPDATING THE SCLERODERMA CLASSIFICATION CRITERIA
Bibliographic record
Abstract
Systemic Sclerosis (Scleroderma, SSc) is a rare and chronic connective tissue disease of unknown etiology. The current classification criteria for SSc were created in 1980 and fail to classify 12% of individuals with SSc who should be classified with its limited forms. A Delphi Consensus exercise of three rounds was conducted among an international team of rheumatologists to determine which items from a list of potential criteria best classify SSc. Cluster analysis was used to reduce the final consensus list to criteria with a best fit. The Canadian Scleroderma Research Group (CSRG) patient database was used to determine the proportion of patients the criteria classify. The Delphi exercise achieved consensus for 18 items and cluster analysis identified criteria filling into four categories: tissue damage, major skin involvement, capillary characteristics and auto-antibodies. The addition of dilated capillaries, telangiectasis, Raynaud’s phenomenon, auto-antibodies, esophogeal dysmotility / dysphagia and calcinosis can classify 94% of the CSRG database. Updated classification criteria will improve disease identification and case definitions for research purposes, contributing to SSc research, patient treatment and prognosis.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".