Characterization of Active Joint Count Trajectories in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
Aim: To describe the longitudinal active joint count (AJC) trajectories in juvenile idiopathic arthritis (JIA). Methods: A retrospective cohort study at two Canadian centres was performed. The longitudinal trajectories of AJC were described using latent growth curve modeling. The association of baseline characteristics stratified by trajectory group was examined by univariate methods. Results: Data were analyzed for 659 children diagnosed with JIA between 1990/03-2009/09. A maximum of 10 years of follow-up data were included in the analysis. Participants were classified into 5 statistically and clinically distinct AJC trajectories by latent GCM. Conclusions: Using a novel longitudinal statistical method we were able to classify patients with JIA based on their pattern of AJC over time. The trajectory classes need to be examined for their relationship to important genetic and biological predictors. Identification of patterns of disease course is important in working towards the development of a clinically relevant outcome-based classification system in JIA.
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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".