Systemic Lupus Erythematosus Disease Activity Index 2000 Responder Index-50: A Reliable Index for Measuring Improvement in Disease Activity
Bibliographic record
Abstract
OBJECTIVE: To test the interrater and intrarater reliability of the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) Responder Index (SRI-50), an index designed to measure ≥ 50% improvement in disease activity between visits in patients with systemic lupus erythematosus. METHODS: This was a multicenter, cross-sectional study with raters from Canada, the United Kingdom, and Argentina. Patient profile scenarios were derived from real adult patients. Ten rheumatologists from university and community hospitals and postdoctoral rheumatology fellows participated. An SRI-50 data retrieval form was used. Each rheumatologist scored SLEDAI-2K at the baseline visit and SRI-50 on followup visit, for the same patients, on 2 occasions 2 weeks apart. Physician global assessment (PGA) was determined on a numerical scale at baseline visit and a Likert scale on followup visit. Interrater and intrarater reliability was assessed using intraclass correlation coefficient (ICC) and kappa statistics whenever applicable. RESULTS: Forty patient profiles were created. The ICC performed on 80 patient profiles for interrater ranged from 1.00 for SLEDAI-2K and SRI-50 to 0.96 for PGA. The intrarater ICC for SLEDAI-2K, SRI-50, and PGA scores ranged from 1.00 to 0.86. Substantial agreement was determined for the interrater Likert scale, with a kappa statistic of 0.57. CONCLUSION: The SRI-50 is reliable to assess ≥ 50% improvement in lupus disease activity. Use of the SRI-50 data retrieval form is essential to ensure optimal performance of the SRI-50. SRI-50 can be used by both rheumatologists and trainees and performs equally well in trained as well as untrained rheumatologists.
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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 | 0.012 | 0.020 |
| 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 | 0.000 |
| 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".