Patterns of pain over time among children with juvenile idiopathic arthritis
Bibliographic record
Abstract
OBJECTIVES: Pain is a very common symptom of juvenile idiopathic arthritis (JIA). Disease activity alone cannot explain symptoms of pain in all children, suggesting other factors may be relevant. The objectives of this study were to describe the different patterns of pain experienced over time in children with JIA and to identify predictors of which children are likely to experience ongoing pain. METHODS: This study used longitudinal-data from patients (aged 1-16 years) with new-onset JIA. Baseline and up to 5-year follow-up pain data from the Childhood Arthritis Prospective Study (CAPS) were used. A two-step approach was adopted. First, pain trajectories were modelled using a discrete mixture model. Second, multinomial logistic regression was used to determine the association between variables and trajectories. RESULTS: Data from 851 individuals were included (4 years, median follow-up). A three-group trajectory model was identified: consistently low pain (n=453), improved pain (n=254) and consistently high pain (n=144). Children with improved pain or consistently high pain differed on average at baseline from consistently low pain. Older age at onset, poor function/disability and longer disease duration at baseline were associated with consistently high pain compared with consistently low pain. Early increases in pain and poor function/disability were also associated with consistently high pain compared with consistently low pain. CONCLUSIONS: This study has identified routinely collected clinical factors, which may indicate those individuals with JIA at risk of poor pain outcomes earlier in disease. Identifying those at highest risk of poor pain outcomes at disease onset may enable targeted pain management strategies to be implemented early in disease thus reducing the risk of poor pain outcomes.
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 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 | 0.000 | 0.000 |
| 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".