Disease activity trajectories in paediatric lupus and associations with socioeconomic factors and patient-reported pain
Bibliographic record
Abstract
OBJECTIVE: Using data from participants with paediatric SLE (pSLE) in the Childhood Arthritis and Rheumatology Research Alliance Registry, we aimed to: (1) describe 2-year disease activity trajectories, measured by the SLE Disease Activity Index 2000 (SLEDAI 2K); (2) identify characteristics associated with each trajectory and (3) assess achievement of lupus low disease activity state (LLDAS) and associated baseline characteristics. METHODS: Participants were diagnosed with pSLE within 12 months of baseline visit. Baseline sociodemographic, clinical and treatment characteristics were included in latent trajectory analyses. Associations between patient characteristics with trajectory groups and LLDAS were analysed with multinomial generalised logistic regression modelling. RESULTS: 1002 patients were screened; 553 were included for SLEDAI 2K and 269 for LLDAS analyses. SLEDAI 2K trajectories included (T1) low and stable, (T2) high and decreasing, (T3) intermediate and stable. In multinomial generalised logistic regression, baseline SLEDAI 2K score and insurance type were significantly associated with trajectories. 51% (136/269) of patients achieved LLDAS at least once in 24 months as compared with 17% (47/269) at first assessment. LLDAS attainment at both time points was predicted by lower pain interference scores; LLDAS attainment over 24 months was also associated with baseline American College of Rheumatology classification criteria, rituximab use at baseline and highest completed level of parent/guardian education. CONCLUSIONS: Disease activity trajectories in a pSLE cohort were predicted by baseline SLEDAI 2K and insurance. Only half of the patients achieved LLDAS during the 2-year study period, which was predicted by baseline characteristics including pain interference. The relationship between disease activity and socioeconomic factors and pain warrants further investigation to identify modifiable factors to reduce pSLE disease activity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".