11 Development and initial validation of a novel lupus disease activity index to account for glucocorticoids: SLEDAI-2K glucocorticoids index (SGI)
Bibliographic record
Abstract
Background and aims To develop and validate a new index, SLEDAI-2K Glucocorticoids Index (SGI), to accurately describe disease activity while accounting for GC doses. Methods Phase 1: Identification of patient scenarios followed in a longitudinal cohort. Phase 2: Equation’s derivation explaining the association between SLEDAI-2K and GC dose while using physician global assessment (PGA) as Gold Standard. Phase 3: Validation of SGI against SLEDAI-2K in active patients. Scenarios were identified using the top 13 organ involvement combinations, then patients were grouped into 7 categories based on GC dose and 10 patients per category were selected. Scenario information included: SLEDAI-2K score, organ involvement combination and GC dose. 3 rheumatologists ranked disease activity with PGA. An independent cohort was used for the validation in phase 3. We hypothesised that in patients with improvement/worsening by SLEDAI-2K, the change in SLEDAI-2K and SGI will correlate. Results Scenario development is summarised in table 1. 131 scenarios were ranked by 3 rheumatologists leading to 393 records. Perfect LS agreement was achieved; ICC (2, k) of 0.89 (95% CI: 0.83, 0.89). A quadratic linear regression model relating GC and SLEDAI-2K was structured; SGI score=SLEDAI-2K score+[3.65+0.29*GC–0.0027(GC*GC)]. The weight score of GC doses was derived (Table 2). Construct Validity: 109 of the 158 patients improved, 38 remained unchanged, 11 worsened. SLEDAI-2K and SGI correlated highly (r=0.87) and changed in the same direction in patients with improvement/worsening proving the validity of SGI. Conclusions We developed and validated a novel lupus disease activity index, SGI, that describes disease activity while accounting for GC dose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".