Correlation Between Interferon Response Gene Score and Disease Activity in Juvenile Dermatomyositis
Bibliographic record
Abstract
Objective Type 1 interferons (IFNs) have been identified as potentially important measures of disease activity in juvenile dermatomyositis (JDM). An IFN response gene (IRG) score has been defined using NanoString technology and appears to correlate with disease activity in cross-sectional samples of patients with JDM. This study aimed to determine if there is evidence of a correlation between disease activity and IRG score in patients with JDM, both early in the disease course and longitudinally. Methods All patients attending the JDM clinic at The Hospital for Sick Children (SickKids), in Toronto, Canada, were approached to enroll in the Childhood Arthritis and Rheumatic Diseases (CARD) biobank. We identified patients with a diagnosis of JDM, enrolled between January 2015 and June 2022. NanoString IRG score was calculated from extracted RNA. The modified Disease Activity Score was calculated based on clinical data collected prospectively through SickKids’s JDM registry. Spearman correlation was calculated using all enrollment visit samples, and linear mixed model regression was used for subjects with multiple samples. Results Forty-three subjects with 87 biosamples were identified, including 18 treatment-naïve subjects. Spearman correlation at the enrollment visit was strong ( r s 0.78) with similar results seen in the treatment-naïve cohort ( r s 0.63). This relationship persisted over time, with linear mixed modeling of the treatment-naïve cohort showing β coefficient for the IRG score of 0.004 with P < 0.001. Conclusion This study shows evidence of a significant correlation between IRG score and disease activity, which is maintained over time. This highlights the potential for IRG score to be an important biomarker in JDM.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".