<scp>Parent‐Reported</scp> Medication Side Effects and Their Impact on <scp>Health‐Related</scp> Quality of Life in Children With Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To describe the frequency and severity of parent-reported medication side effects (SEs) in children with juvenile idiopathic arthritis (JIA) relative to physician-reported actionable adverse events (AEs), and to assess their impact on health-related quality of life (HRQoL). METHODS: Newly diagnosed JIA patients recruited between 2017 and 2019 to the Canadian Alliance of Pediatric Rheumatology Investigators (CAPRI) Registry were included. Parents reported presence and severity (0 = no problem, 10 = very severe) of medication SEs at every clinic visit. Physicians were asked to report any actionable AE. HRQoL was assessed using the Quality of My Life (QoML) questionnaire (0 = the worst, 10 = the best) and parent's global assessment (0 = very well, 10 = very poor). Analyses included proportion of visits with SEs or actionable AEs, cumulative incidence by Kaplan-Meier methods, and HRQoL impact measured with longitudinal mixed-effects models. RESULTS: SEs were reported at 371 of 884 (42%) visits (95% confidence interval [95% CI] 39, 45%) in 249 patients, with a median of 2 SEs per visit (interquartile range [IQR] 1-3), and median severity of 3 (IQR 1.5-5). Most SEs were gastrointestinal (32.5% of visits) or behavioral/psychiatric (22.4%). SE frequency was lowest with nonsteroidal antiinflammatory drugs alone (34.7%) and highest with prednisone and methotrexate combinations (66%). SE cumulative incidence was 67% (95% CI 59, 75) within 1 year of diagnosis, and 36% (95% CI 28, 44) for actionable AEs. Parent global and QoML scores were worse with SEs present; the impact persisted after adjusting for pain and number of active joints. CONCLUSION: Parents report that two-thirds of children with JIA experience SEs impacting their HRQoL within 1 year of diagnosis. SE mitigation strategies are needed in managing JIA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".