Proceedings of the 25th European Paediatric Rheumatology Congress (PReS 2018)
Bibliographic record
Abstract
Introduction: Poor sleep quality is common in children with juvenile idiopathic arthritis (JIA). Sleep quality is affected by the child’s activity in disease and experience of pain. Psychological variables seem to have an impact on the sleep quality as well. Objectives: The aim of this study was to examine how pain, disease activity, and psychological wellbeing differs in children with JIA with poor or normal sleep quality, and the likelihood of these variables on having poor sleep quality. Methods: In the outpatient clinic patients diagnosed with JIA age 6-16 were included. Child and a parent completed questionnaires regarding the child’s sleep quality in The Children’s Sleep Habits Questionnaire (CSHQ), anxiety symptoms in Spence Children’s Anxiety Scale (SCAS), wellbeing in WHO-5 Wellbeing Index (WHO-5)), positive and negative mood in The Positive and Negative Affect Schedule (PANAS). Pain intensity was measured using the VAS 1-10 scale from the Juvenile Arthritis Multidimensional Assessment Report (JAMAR). Disease activity was calculated as JADAS-27. Exclusion criteria were JIA in remission off medication, co-morbidity, non- Danish speaking patients. Results: In total 62 patients were included. Results indicated that 55% of the children reported poor sleep quality (CSHQ>41). Children with poor sleep quality reported significant lower level of wellbeing and higher levels of anxiety, negative mood, and pain intensity, and showed higher level of disease activity (p =.02-.001). A logistic regression was performed to ascertain the effect of pain, disease activity, wellbeing, anxiety, and negative mood on the likelihood, that the children had poor sleep quality. The model was statistically significant (chi2 (4) = 16.50, p=.006) and explained 36.4% of the variance in poor sleep quality and correctly classified 75.5% of cases. Increasing level of anxiety symptoms was significantly associated with an increased likelihood of poor sleep quality (p=.01). None of the other predictor variables made a unique, significant contribution. Conclusion: Disease activity, pain, and psychological wellbeing were negatively affected in children with poor sleep quality. Especially the level of anxiety symptoms were a significant predictor of having poor sleep quality, which highlights the importance of focusing on the impact of anxiety and worry on children’s sleep quality.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.206 | 0.085 |
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".