The Pediatric Rheumatology European Society/American College of Rheumatology/European League against Rheumatism provisional classification criteria for juvenile systemic sclerosis
Bibliographic record
Abstract
OBJECTIVE: To develop criteria for the classification of systemic sclerosis (SSc) in children (juvenile SSc). METHODS: The study consisted of 3 phases: 1) collection of data on the signs and symptoms of actual patients with juvenile SSc that are useful for defining involvement of a particular organ; 2) selection of the parameters essential for the classification of juvenile SSc and preparation of a set of provisional classification criteria (PCC) using 2 Delphi surveys; 3) consensus conference consisting of 2 steps: discussion and rating of clinical profiles of 160 patients with definite juvenile SSc, possible juvenile SSc, or other fibrosing diseases as "having or not having juvenile SSc," using nominal group technique, and defining those PCC with the best statistical performance and highest face validity by using the clinical profiles of patients with definite juvenile SSc as the gold standard. RESULTS: In phase 1, 55 centers submitted clinical data on 153 patients with juvenile SSc. A total of 48 signs and symptoms were derived from these patient data and were used to define 9 organ system categories (cutaneous, vascular, gastrointestinal, respiratory, renal, cardiac, neurologic, musculoskeletal, and serologic). During phase 2, these were reduced to 21 criteria (3 major criteria [Raynaud's phenomenon, proximal skin sclerosis/induration of the skin, and sclerodactyly] and 18 minor criteria) and combined to generate 86 different PCC. At the consensus conference, these 86 definitions were tested on the case profiles of 127 patients with juvenile SSc. The PCC with the highest ranking were proximal sclerosis/induration and at least 2 minor criteria. CONCLUSION: These provisional classification criteria for juvenile SSc will help standardize the conduct of clinical research, epidemiologic and outcome studies, and therapeutic trials.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".