From Childhood to Adulthood: The Trajectory of Damage in Patients With Juvenile‐Onset Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To determine the longitudinal damage trajectory of patients with juvenile-onset systemic lupus erythematosus (SLE), and to identify baseline and disease course predictors of damage trajectory. METHODS: This is a retrospective inception cohort. Longitudinal pediatric-age data were obtained from a juvenile-onset SLE research database, while adult-age data were obtained from either a research database or patients' charts. Baseline factors were tested as predictors. Time-varying factors were lagged 6-24 months before a visit for testing their predictive effects. The longitudinal damage trajectory was modeled using a weighted generalized estimating equation. RESULTS: This study cohort consisted of 473 subjects, with followup to 26 years. A total of 65% of patients were ages >18 years at last followup. Cataracts (14%), avascular necrosis (10%), and osteoporosis (5%) were the most common items of damage. Two patients had myocardial infarctions. Baseline features, self-reported ethnicity (Afro-Caribbean), earlier time periods of diagnosis, and the presence of a life-threatening major organ manifestation (lupus nephritis class III-V, cerebrovascular accidents, major organ vasculitis, pulmonary hemorrhage, or myocarditis), were associated with greater damage. Throughout the disease course, an acute confusional state, lupus headache, and fever predicted subsequent increases in the damage trajectory. A higher prednisone dose and exposure to cyclophosphamide also predicted subsequent increases in the damage trajectory. Antimalarial exposure was protective against an increase in damage trajectory. CONCLUSION: Patients with juvenile-onset SLE accrue damage steadily into adulthood. Baseline factors predict greater damage and/or influence the evolution of the damage trajectory. Additionally, SLE clinical features and therapies during the course of disease predict additional changes in the damage trajectory.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".