The Elephant in the Rheum: Time to Address Mental Health as a Priority in Pediatric Rheumatology
Bibliographic record
Abstract
Mental health, which encompasses psychological and emotional well-being, is increasingly being recognized as an important aspect of overall health in youth with chronic disease. Particularly in this time of the mental health crisis in children due in large part to the coronavirus disease 2019 (COVID-19) pandemic,1 with evidence of adverse effects on managing rheumatologic disease,2 addressing mental health issues has become critical. In the pediatric rheumatology population, mental health problems have been shown to affect quality of life, educational attainment, and disease-related outcomes such as medication adherence, healthcare utilization, and transition.3 Although patients with juvenile idiopathic arthritis (JIA) represent the largest disease group in pediatric rheumatology, there are gaps in our understanding of mental health effects in JIA, and research to optimize mental health for these patients is needed. In this issue of The Journal of Rheumatology , Li et al present the results of an interesting cross-sectional study of children with JIA aged 12 to 18 years, examining rates of depressive and anxiety symptoms, and their association with demographic and disease measures.4 The sample included 80 Canadian adolescents who presented to a tertiary care pediatric hospital and had JIA diagnoses of varying subtypes for at least 6 months. Based on clinical threshold scores on the self-reported Revised Child Anxiety and Depression Scale (RCADS), the numbers of youth endorsing symptoms consistent with a mental health disorder were described according to 6 subscales (ie, separation anxiety, social phobia, generalized anxiety, panic disorder, obsessive-compulsive disorder, and major depressive disorder). Differences in demographic and disease-related factors (eg, patient-reported disease activity, physician global assessment of disease activity [PGA], widespread pain, joint and enthesitis count) between those with and without clinically elevated mental health symptoms were also explored. Results indicated a recruitment rate of 98%, and a patient sample consisting of … Address correspondence to Dr. A. Knight, Hospital for Sick Children, Division of Rheumatology, Department of Pediatrics, 555 University Ave, Toronto, ON M5G 1X8, Canada. Email: andrea.knight{at}sickkids.ca.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".