Persistent Disease Activity Remains a Burden for Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Persistent systemic lupus erythematosus (SLE) disease activity is associated with increased morbidity and mortality. In a multicenter cohort of patients with prevalent SLE, we described persistence, patterns, and predictors of change in disease activity over time. METHODS: Based on SLE Disease Activity Index (SLEDAI)-2K scores at cohort entry, patients were classified into 4 groups: low (score < 4; LOW), moderate (4 to < 6; MOD), moderately high (6 to ≤ 10; MHIGH), and very high (> 10; VHIGH). Multivariable linear and longitudinal mixed linear regression models were used to identify predictors of change over time in SLEDAI-2K. RESULTS: There were 2019 participants, with declining followup data over 5 years (1326, 580, 274, 186, and 148 patients, respectively). At cohort entry, mean (± SD) age was 42 (± 17) years, disease duration 11 (± 10) years, and 90% were female. The 4 groups included 44% LOW (n = 891), 20% MOD (n = 400), 22% MHIGH (n = 442), and 14% VHIGH (n = 286); therefore, 36% had clinically important SLE activity. The proportion of patients in the LOW group at entry who moved to a higher activity level varied from 30% (167/557) at 1 year, to 49% (41/83) at 3 years, and 54% (30/56) at 5 years. Among 181 patients with MOD to VHIGH entry activity and 3 years of followup, 116 (64.1%) remained active. In all analyses, only higher SLEDAI-2K at cohort entry remained a significant predictor of higher SLEDAI-2K in subsequent years. CONCLUSION: Higher SLEDAI-2K at study entry was the single major independent predictor of higher SLEDAI-2K over time, reflecting frequent persistence of active disease, even in patients with longstanding disease. This highlights gaps in the optimal treatment of SLE.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 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".