A novel lupus activity index accounting for glucocorticoids: SLEDAI-2K glucocorticoid index
Bibliographic record
Abstract
Objective: To develop and validate a modification of SLEDAI-2K to accurately describe disease activity while accounting for glucocorticoid (GC) doses. Methods: The first two phases focused on the development of the index. Phase 1: identification of scenarios of real patients seen prospectively in a longitudinal cohort. Phase 2: derivation of an equation that explains the association between SLEDAI-2K and GC doses using physician global assessment as the external construct. Phase 3: comparison of SLEDAI-2K and SLEDAI-2K GC (SLEDAI-2KG), using different cut-off points (4-7), in identifying responders in response to therapy. Results: In phase 1, 150 scenarios with different organ involvement and a range of GC doses were identified. In phase 2, three rheumatologists ranked disease activity using physician global assessment. A quadratic linear regression model relating GC doses and SLEDAI-2K resulted in the following equation: SLEDAI-2KG score = SLEDAI-2K score + [0.32 × GC - 0.0031 × GC2]. The weighted score of different GC doses was derived. In phase 3, SLEDAI-2KG identified more responders in a total of 111 patients at 6 months (84 vs 93%) and at 12 months (76 vs 92%) compared with SLEDAI-2K. SLEDAI-2KG performances were superior to SLEDAI-2K with all cut-off points (5-7). Conclusion: We developed a modification of SLEDAI-2K, SLEDAI-2KG, that describes disease activity while accounting for GC dose category. SLEDAI-2KG identifies more responders compared with SLEDAI-2K.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".