Predictors of persisting pain in children with Juvenile Idiopathic Arthritis: a case control study nested in the ReACCh-Out cohort
Bibliographic record
Abstract
BACKGROUND: To identify baseline predictors of persisting pain in children with Juvenile Idiopathic Arthritis (JIA), relative to patients with JIA who had similar baseline levels of pain but in whom the pain did not persist. METHODS: We used data from the Research in Arthritis in Canadian Children emphasizing Outcomes (ReACCh-Out) inception cohort to compare cases of 'moderate persisting pain' with controls of 'moderate decreasing pain'. Moderate pain was defined as a Visual Analogue Scale (VAS) for pain measurement score of > 3.5 cm. Follow-up was minimum 3 years. Univariate and Multivariate logistic regression models ascertained baseline predictors of persisting pain. RESULTS: A total of 31 cases and 118 controls were included. Mean pain scores at baseline were 6.4 (SD 1.6) for cases and 5.9 (1.5) for controls. A greater proportion of cases than controls were females (77.4% vs 65.0%) with rheumatoid factor positive polyarthritis (12.9% vs 4.2%) or undifferentiated JIA (22.6% vs 8.5%). Oligoarthritis was less frequent in cases than controls (9.7% vs 33%). At baseline, cases had more active joints (mean of 11.4 vs 7.7) and more sites of enthesitis (4.6 vs 0.7) than controls. In the final multivariate regression model, enthesitis count at baseline (OR 1.40, CI 95% 1.19-1.76), female sex (4.14, 1.33-16.83), and the overall Quality of My Life (QoML) baseline score (0.82, 0.69-0.98) predicted development of persisting pain. CONCLUSIONS: Among newly diagnosed children with JIA with moderate pain, female sex, lower overall quality of life, and higher enthesitis counts at baseline predicted development of persisting pain. If our findings are confirmed, patients with these characteristics may be candidates for interventions to prevent development of chronic pain.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".