The Cutaneous Assessment Tool: development and reliability in juvenile idiopathic inflammatory myopathy
Bibliographic record
Abstract
OBJECTIVES: Clinical care and therapeutic trials in idiopathic inflammatory myopathies (IIM) require accurate and consistent assessment of cutaneous involvement. The Cutaneous Assessment Tool (CAT) was designed to measure skin activity and damage in IIM. We describe the development and inter-rater reliability of the CAT, and the frequency of lesions endorsed in a large population of juvenile IIM patients. METHODS: The CAT includes 10 activity, 4 damage and 7 combined lesions. Thirty-two photographic slides depicting IIM skin lesions were assessed by 11 raters. One hundred and twenty-three children were assessed by 11 paediatric rheumatologists at 10 centres. Inter-rater reliability was assessed using simple agreements and intra-class correlation coefficients (ICC). RESULTS: Simple agreements in recognizing lesions as present or absent were generally high (0.5-1.0). ICCs for CAT lesions were moderate (0.4-0.75) in both slides and real patients. ICCs for the CAT activity and damage scores were 0.71 and 0.81, respectively. CAT activity scores ranged from 0 to 44 (median 7, potential range 0-96) and CAT damage scores ranged from 0 to 13 (median 1, potential range 0-22). The most common cutaneous lesions endorsed were periungual capillary loop changes (63%), Gottron's papules/sign (53%), heliotrope rash (49%) and malar/facial erythema (49%). CONCLUSIONS: Total CAT activity and damage scores have moderate to good reliability. Assessors generally agree on the presence of a variety of cutaneous lesions. The CAT is a promising, semi-quantitative tool to comprehensively assess skin disease activity and damage in IIM.
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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 | 0.032 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".