Correlation of a Modified Disease Activity Score (DAS) with the Validated Original DAS in Patients with Juvenile Dermatomyositis
Bibliographic record
Abstract
Objective. Juvenile dermatomyositis (JDM) is a rare disease in children that is treatable, but patients may suffer from long-term effects. Clinical trials are needed to find better treatments for affected patients. Among validated tools for evaluating disease activity clinically is the Disease Activity Score (DAS), but it is not routinely collected in all clinics. We developed a modified DAS (DASmod), which can be scored using data routinely collected by our clinical staff and has been used in previous studies. The aim of this study was to determine if our DASmod correlates with the validated DAS in patients with JDM. Methods. In this study, we used DASmod (scored 0–12) and DAS (scored 0–20) scores for patients with JDM in our clinic. We analyzed the correlation between the DASmod and the validated DAS. Results. For 51 patients seen in our JDM clinic, the median (IQR) DASmod score was 2.0 (0–4.0) and the DAS score was 3.0 (0–5.5). Scores on the 2 tools were highly positively correlated (r = 0.94, P < 0.001, 95% CI 0.89–0.96). The linear regression was significant [R2 = 0.88, F (1, 49) = 357.60, P < 0.001] and in this dataset, the tools can be used interchangeably with the regression equation: DAS score = –0.26 + 1.5*DASmod. Conclusion. If the regression equation from this dataset is successfully tested against future datasets, then further research collaborations between centers that collect different data related to disease activity in children with JDM will be facilitated.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".