Measuring Disease Activity and Damage with Validated Metrics: A Systematic Review on Mortality and Damage in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To identify the effect of disease activity and damage, measured by validated indices, on mortality and damage accrual, in order to inform upcoming Canadian systemic lupus erythematosus (SLE) recommendations. METHODS: Following GRADE (Grading of Recommendations Assessment, Development and Evaluation) methodology to fill in evidence-to-decision tables to create recommendations for "minimal investigations needed to monitor SLE patients at baseline and subsequent visits," a systematic literature review was performed. The effect of disease activity and damage, measured by validated metrics, on mortality and damage was systematically reviewed, with metaanalyses performed when available. RESULTS: A title/abstract screen of 5599 articles identified 816 articles for full paper review, with 102 meeting inclusion criteria and 53 with extractable data. Thirty-three articles describing outcomes related to disease activity and 20 articles related to damage were identified. Mortality was associated with higher SLE Disease Activity Index-2000 scores in 6 studies (HR 1.14, 95% CI 1.06-1.22) and higher Systemic Lupus International Collaborating Clinics/ACR Damage Index scores in 6 studies (HR 1.53, 95% CI 1.28-1.83). Higher SLE Activity Measure scores were associated with increased risk of damage in 3 studies (OR 1.06, 95% CI 1.04-1.08). British Isles Lupus Assessment Group was associated with mortality in 1 study with HR of 1.15. CONCLUSION: Active SLE disease and damage are associated with and predict greater mortality and damage. The use of validated disease activity and damage metrics is important in the assessment of disease activity and damage and will inform upcoming Canadian recommendations for the assessment 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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".