Predictive Correlates of Arthritis and Joint Damage in Systemic Lupus Erythematosus: A Multinational Prospective Cohort Study
Bibliographic record
Abstract
Abstract Objectives To determine the prevalence and predictive correlates of arthritis and joint damage in systemic lupus erythematosus (SLE) patients in the Asia-Pacific Lupus Collaboration (APLC) cohort, and to determine their impact on health-related quality of life (HRQoL). Methods SLE patient data (2013–2020) were collected from the prospective multinational APLC cohort. We defined arthritis according to the SLE Disease Assessment Index (SLEDAI-2K) definition of persistent arthritis as arthritis in ≥2 consecutive visits, and joint damage according to the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) definition (deforming or erosive arthritis). HRQoL was measured by Short Form Survey (SF36). Descriptive statistics, univariable and multivariable Cox hazard models, and Kaplan–Meier analyses were performed. Results During median 2.5 (1.0–5.1) years of follow-up, 803/4106 (19.6%) patients had arthritis at least once, and 18/3383 (0.53%) accrued joint damage. Patients with arthritis were more likely to be female, Caucasian, current smokers at enrolment, and less like to have tertiary education; they also had higher overall disease activity, and lower physical and mental HRQoL. Kaplan–Meier analysis demonstrated that joint damage was more likely in patients with arthritis. Persistent arthritis and longer follow-up were risk factors for joint damage accrual; being from high-income countries was protective. Patients with joint damage also had worse physical HRQoL. Conclusion Arthritis in the APLC cohort was infrequent compared with other cohorts and was associated with smoking, higher overall disease activity, and damage accrual across multiple domains. Presence of arthritis significantly impacted physical and mental HRQoL. Joint damage was strongly predicted by persistent arthritis.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".