Performance of a New Instrument for the Measurement of Systemic Lupus Erythematosus Disease Activity: The SLE-DAS
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is a chronic systemic autoimmune disease that affects multiple organ systems and manifests in a relapsing-remitting pattern. Consequently, it is paramount for rheumatologists to assess disease activity, identify flare-ups, and establish treatment goals for patients with SLE. In 2019, the Systemic Lupus Erythematosus Disease Activity Score (SLE-DAS) was introduced as a novel tool for measuring disease activity. This tool refines the parameters of the established SLE Disease Activity Index 2000 (SLEDAI-2K) to enhance the assessment process. This review aims to provide an introduction to the Systemic Lupus Erythematosus Disease Activity Score (SLE-DAS) and summarizes research on its development, its comparison with existing disease activity measures, and its performance in clinical settings. Literature searches on PubMed using the keyword "SLE-DAS" were conducted, covering publications from March 2019 to September 2023. Studies that compared SLE-DAS with other SLE disease activity measurement tools were reviewed. Findings indicated that SLE-DAS consistently performs on par with, and sometimes better than, traditional measures in assessing clinically meaningful changes, patient improvement, disease activity, health-related quality of life, hospitalization rates, and disease flare-ups. The association between SLE-DAS and mortality rates among patients with SLE, however, remains to be further explored. Although SLE-DAS is a promising and potentially effective tool for measuring SLE disease activity, additional research is needed to confirm its effectiveness and broaden its clinical use.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".