Trajectories of pain severity in juvenile idiopathic arthritis: results from the Research in Arthritis in Canadian Children Emphasizing Outcomes cohort
Bibliographic record
Abstract
We studied children enrolled within 90 days of juvenile idiopathic arthritis diagnosis in the Research in Arthritis in Canadian Children Emphasizing Outcomes (ReACCh-Out) prospective inception cohort to identify longitudinal trajectories of pain severity and features that may predict pain trajectory at diagnosis. A total of 1062 participants were followed a median of 24.3 months (interquartile range = 16.0-37.1 months). Latent trajectory analysis of pain severity, measured in a 100-mm visual analogue scale, identified 5 distinct trajectories: (1) mild-decreasing pain (56.2% of the cohort); (2) moderate-decreasing pain (28.6%); (3) chronically moderate pain (7.4%); (4) minimal pain (4.0%); and (5) mild-increasing pain (3.7%). Mean disability and quality of life scores roughly paralleled the pain severity trajectories. At baseline, children with chronically moderate pain, compared to those with moderate-decreasing pain, were older (mean 10.0 vs 8.5 years, P = 0.01) and had higher active joint counts (mean 10.0 vs 7.2 joints, P = 0.06). Children with mild-increasing pain had lower joint counts than children with mild-decreasing pain (2.3 vs 5.2 joints, P < 0.001). Although most children with juvenile idiopathic arthritis in this cohort had mild or moderate initial levels of pain that decreased quickly, about 1 in 10 children had concerning pain trajectories (chronically moderate pain and mild-increasing pain). Systematic periodic assessment of pain severity in the months after diagnosis may help identify these concerning pain trajectories early and lay out appropriate pain management plans. Focused research into the factors leading to these concerning trajectories may help prevent them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".