Systemic Lupus Erythematosus Disease Activity Index 2000 Responder Index 50: sensitivity to response at 6 and 12 months
Bibliographic record
Abstract
OBJECTIVE: The SLEDAI 2000 (SLEDAI-2K) Responder Index 50 (SRI-50) is a novel index that measures ≥ 50% in each of the 24 descriptors of SLEDAI-2K and generates a total score reflecting disease activity overall. The SLE Responder Index (SRI) has been successfully used to identify responders in recent trials. This is the first study to evaluate the ability of SRI-50 to identify responders, defined as patients who had a clinically important improvement over their baseline value over 12 months. We compared the performance of SRI-50 with that of SLEDAI-2K and SRI at 6 and 12 months in identifying responders. METHODS: Patients with active disease were followed for 6-12 months and assessed using SLEDAI-2K, British Isles Lupus Assessment Group and Physician Global Assessment. We identified SLEDAI-2K responders, SRI-50 responders and SLE responders at 6 and 12 months. We determined whether patients who are defined as SRI-50 responders are true responders when SRI is considered the gold standard. RESULTS: Among the 103 patients studied, the percentage of responders at 6 and 12 months was 44 and 51% when determined by SLEDAI-2K and 43 and 51% by SRI, respectively. The percentage of SRI-50 responders at 6 and 12 months was 51 and 58%, respectively. CONCLUSION: SRI-50 identified more responders compared with SLEDAI-2K and SRI at 6 and 12 months. SRI-50 is a valid responder index that can be used independently to identify patients with true clinically important improvement.
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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 | 0.004 | 0.009 |
| 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 | 0.001 | 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".