Description of Active Joint Count Trajectories in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To describe the trajectories of longitudinal joint disease activity in juvenile idiopathic arthritis (JIA), and to examine associations of clinical and laboratory characteristics with the identified trajectories. METHODS: A retrospective cohort study at 2 Canadian centers was performed. The longitudinal trajectories of active joint counts were described in a proof-of-concept study using a latent growth curve analysis. Baseline patient characteristics were compared across trajectory groups. RESULTS: Data were analyzed on 659 children diagnosed with JIA between March 1980 and September 2009. The median age at diagnosis was 10.0 years (interquartile range 3.7-13.4) and 61% (402/659) were female. The International League of Associations for Rheumatology (ILAR) diagnoses were as follows: oligoarthritis (36%), enthesitis-related arthritis (20%), rheumatoid factor (RF)-negative polyarthritis (13%), undifferentiated arthritis (12%), psoriatic arthritis (8%), systemic arthritis (7%), and RF-positive polyarthritis (4%). Based on the trajectories of their active joint counts, the 659 patients were each classified in 1 of 5 latent classes (which can be described as high decreasing, moderate increasing, persistent moderate, persistent low, and minimal joint activity). These latent classes were clinically and statistically distinct from the ILAR categories. CONCLUSION: In this proof-of-concept study, in which we used an analytic methodology in a novel way, we identified 5 clinically and statistically distinct trajectories of disease course. The subsets of patients within each class were different from those described by the ILAR classification criteria. This successful application of this method supports its use in a chronic disease with a fluctuating course such as JIA. These methods should be expanded for the purposes of predictive modeling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".