Currently recommended skin scores correlate highly in the assessment of patients with Juvenile Dermatomyositis (JDM)
Bibliographic record
Abstract
BACKGROUND: Juvenile Dermatomyositis (JDM) is a rare, chronic, and life-threatening childhood autoimmune disease. Currently, there are recommended, reliable and validated measurement tools for assessment of skin disease activity in JDM including the Disease Activity Score (skinDAS), Cutaneous Assessment Tool (CAT), and the Cutaneous Dermatomyositis Disease Area and Severity Index (CDASI). The Physician's global assessment skin visual analog scale (Skin VAS) is also widely used for skin activity in JDM. For the purpose of comparative international studies, we wanted to compare these tools to the Physician's skin VAS (as a standard) to identify which performs better. OBJECTIVES: We sought to compare the correlations of these scoring tools, and separately assess the responsiveness each tool demonstrates following patient treatment, in order to see if one tool may be preferred. This was determined by assessing how well these tools correlate with each other, and the Physician's skin VAS over time, as well as the responsiveness of each tool after patient treatment. METHODS: , 2018) and all follow-up office visits at the Juvenile Dermatomyositis Clinic. Following baseline visits, patients were followed up as clinically indicated. A subset of newly diagnosed patients (inception cohort) was identified. Correlations were assessed at the baseline visit and over time for the whole cohort. The correlations over time were derived using Generalized Estimating Equations (GEEs). Standardized response means with 95% confidence intervals were calculated to test score responsiveness for the nested inception cohort. RESULTS: The skinDAS, CAT and CDASI all correlated highly with each other and with the Physician's skin VAS. The three scoring tools accurately reflected Physician's skin VAS scores over time. In addition, all tools showed moderate to high responsiveness following treatment. CONCLUSION: All studied skin score tools performed well in our study and appear to be useful. Since no tool far outperforms the others, arbitrary consensus will be needed to select a single standard measurement tool for the purposes of efficiency and global comparability.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".