Resilience and its associations in children with Systemic Lupus Erythematosus and Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
BACKGROUND: Resilience has been shown to be associated with better psychological outcomes and ability to cope with negative and traumatic events in the healthcare setting. Therefore, in this study, we aimed to evaluate resilience and its association with disease activity and health-related quality of life (HRQOL) in children with Systemic Lupus Erythematosus (SLE) and Juvenile Idiopathic Arthritis (JIA). FINDINGS: Patients with diagnoses of SLE or JIA were recruited. We collected: demographic data, medical history and physical examination, physician and patient global health assessments, Patient Reported Outcome Measurement Information System questionnaires, Connor Davidson Resilience Scale 10 (CD-RISC 10), Systemic Lupus Erythematosus Disease Activity Index, and clinical Juvenile Arthritis Disease Activity Score 10. Descriptive statistics were calculated, and PROMIS raw scores were converted to T-scores. Spearman's correlations were performed, with statistical significance set to p < 0.05. 47 study subjects were recruited. The average CD-RISC 10 score in SLE was 24.4, and in JIA was 25.2. In children with SLE, CD-RISC 10 was correlated with disease activity and inversely correlated with anxiety. In children with JIA, resilience was inversely associated with fatigue, and positively correlated with mobility and peer relationships. CONCLUSIONS: In children with SLE and JIA, resilience is lower than in the general population. Further, our results suggest that interventions to increase resilience may improve the HRQOL of children with rheumatic disease. Ongoing study of the importance of resilience in this population, as well as interventions to increase resilience, will be an important area of future research in children with SLE and 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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".