Predictors of Organ Damage in Systemic Lupus Erythematosus in the Asia Pacific Region: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Irreversible organ damage is common in patients with systemic lupus erythematosus (SLE). Despite evidence of increased prevalence and severity of SLE in Asia Pacific, organ damage is less well studied in this region. This systematic review aims to identify predictors of organ damage in SLE in the Asia Pacific region. METHODS: We searched Medline, PubMed, Embase, and Web of Science for observational studies on organ damage in adult patients with SLE in Asia Pacific from August 31, to September 5, 2022. Study selection and data extraction were completed by two independent reviewers using Covidence systematic review software. Risk of bias was assessed using the Newcastle-Ottawa Scale and Joanna Briggs Institute tool. Significant results from univariable and multivariable analyses were synthesized from included studies. RESULTS: Thirty-eight eligible studies were selected from 1999 to 2022; 22 (58%) of these reported organ damage at study enrollment and 19 (50%) reported damage accrual, as measured by the Systemic Lupus International Collaborating Clinic/American College of Rheumatology Damage Index. Factors predictive of organ damage included older age, glucocorticoid use, longer disease duration, and disease activity. Lupus nephritis was a risk factor for renal and overall damage accrual. Hydroxychloroquine was protective against overall organ damage. CONCLUSION: Predictors of organ damage in SLE in Asia Pacific are similar to other regions. Although glucocorticoid use is a modifiable risk factor for organ damage, the impact of immunosuppressives and biologic therapies needs further investigation. Effective strategies in early disease are needed to minimize initial organ damage as it predicts subsequent damage accrual.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".