From Childhood to Adulthood: Disease Activity Trajectories in Childhood‐Onset Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: No previous study has studied the longitudinal disease course of childhood-onset systemic lupus erythematosus (cSLE). Our objectives are to assess distinguishable differences in disease activity trajectories in cSLE patients, determine baseline factors predictive of disease trajectory membership, and assess if the different disease activity trajectories are associated with different damage trajectories. METHODS: This is a retrospective, longitudinal inception cohort of cSLE patients. Patients were followed from diagnosis as children, until they were adults. SLE disease activity was modeled as a latent characteristic, jointly using the Systemic Lupus Erythematosus Disease Activity Index 2000 and prednisone in a Bayesian growth mixture model. Baseline factors were tested for membership prediction of the latent classes of disease trajectories. Differences in damage trajectories by disease activity classes were tested using a mixed model. RESULTS: A total of 473 patients (82% females), with median age at diagnosis of 14.1 years, were studied. We studied 11,992 visits (2,666 patient-years). We identified 5 classes of disease activity trajectories. Baseline major organ involvement, number of American College of Rheumatology criteria, and age at diagnosis predicted memberships into different classes. A higher proportion of Asians was in class 2 compared to class 5. Class 1 was associated with the most accrual of damage, while class 5 was associated with no significant damage accrual, even after 10 years. CONCLUSION: There are 5 distinct latent classes of disease trajectory in patients with cSLE. Membership within disease trajectories is predicted by baseline clinical and demographic factors. Membership in different disease activity trajectory classes is associated with different damage trajectories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".