An International Multispecialty Validation Study of the IgG4‐Related Disease Responder Index
Bibliographic record
Abstract
OBJECTIVE: IgG4-related disease (IgG4-RD) can cause fibroinflammatory lesions in nearly any organ, leading to organ dysfunction and failure. The IgG4-RD Responder Index (RI) was developed to help investigators assess the efficacy of treatment in a structured manner. The aim of this study was to validate the RI in a multinational investigation. METHODS: The RI guides investigators through assessments of disease activity and damage in 25 domains, incorporating higher weights for disease manifestations that require urgent treatment or that worsen despite treatment. After a training exercise, investigators reviewed 12 written IgG4-RD vignettes based on real patients. Investigators calculated both an RI score as well as a physician's global assessment (PhGA) score for each vignette. In a longitudinal assessment, 3 investigators used the RI in 15 patients with newly active disease who were followed up over serial visits after treatment. We assessed interrater and intrarater reliability, precision, validity, and responsiveness. RESULTS: The 26 physician investigators included representatives from 6 specialties and 9 countries. The interrater and intrarater reliability of the RI was strong (0.89 and 0.69, respectively). Correlations (construct validity) between the RI and PhGA were high (Spearman's r = 0.9, P < 0.0001). The RI was sensitive to change (discriminant validity). Following treatment, there was significant improvement in the RI score (mean change 10.5 [95% confidence interval (95% CI) 5.4-12], P < 0.001), which correlated with the change in the PhGA. Urgent disease and damage were captured effectively. DISCUSSION: In this international, multispecialty study, we observed that the RI is a valid and reliable disease activity assessment tool that can be used to measure response to therapy.
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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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".