Quantifying Cutaneous Dermatomyositis: A Novel 3D Image–Based Approach
Bibliographic record
Abstract
Objective Visual examination of skin lesions has considerable subjectivity and interrater variability. This study assessed the feasibility of a 3D image–based assessment of cutaneous disease activity in dermatomyositis (DM). Methods Patients with DM were evaluated in a prospective study at 2 timepoints for skin rash assessment using the Cutaneous Dermatomyositis Disease Area and Severity Index (CDASI) and 3D images. A 3D image disease activity score (3DAS) was calculated based on the percentage of the rashes relative to the total body surface area, multiplied by the degree of rash redness. The construct validity and responsiveness of 3DAS were evaluated using the Spearman correlation coefficient ( r ) against standard CDASI and patient-reported outcome measures (PROMs). A generalized linear regression model assessed the relationship between the 3D image–derived rash area and redness with the CDASI score. Results Twenty-seven patients with DM (81.5% female, 96.3% White; median age 50.0 years) were enrolled. The median (IQR) CDASI score at baseline was 6.0 (IQR 0.0-17.0). For the construct validity, 3DAS correlated strongly with the CDASI ( r = 0.83; P < 0.001) and PROMs. The generalized linear regression analysis identified the rash area and redness from 3D images as significant predictors of the CDASI score. Regarding responsiveness, absolute changes from baseline in the 3DAS correlated strongly with the CDASI score ( r = 0.61; P = 0.004). Conclusion Our results demonstrate favorable validity and responsiveness of the 3D images for evaluating rashes in patients with DM. The 3D image–derived rash area and redness are significant predictors of CDASI scores.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".