Early impacts of the COVID-19 pandemic on children with pediatric rheumatic diseases
Bibliographic record
Abstract
OBJECTIVES: The experiences of children with pediatric rheumatic diseases (PRD) during the initial phase of the COVID-19 pandemic have not been well-documented. We sought to determine the effects of the COVID-19 pandemic on protective behaviors, healthcare access, medication management, and education among an international cross-sectional parental survey of children with PRDs. METHODS: The COVID-19 Global Rheumatology Alliance Patient Experience Survey was distributed online, and parents of children with parental-reported PRD, with or without COVID-19 infection, were eligible to enroll. Respondents described their child's demographics, adoptions of protective behaviors, healthcare access, changes to immunosuppression, and disruptions in schooling. RESULTS: A total of 427 children were included in the analyses. The most common rheumatic disease was juvenile idiopathic arthritis (40.7%), and most children were taking conventional synthetic diseasemodifying antirheumatic drugs (DMARDs) (54.6%) and/or biologic DMARDs (51.8%). A diagnosis of COVID-19 was reported in five children (1.2%), none of whom required hospitalization. Seventeen children (4.0%) had stopped or delayed their drugs due to concern for immunosuppression, most commonly glucocorticoids. Almost all families adopted behaviors to protect their children from COVID-19, including quarantining, reported by 96.0% of participants. In addition, 98.3% of full-time students experienced disruptions in their education, including cancelations of classes and transitions to virtual classrooms. CONCLUSION: Despite the low numbers of children with PRDs who developed COVID-19 in this cohort, most experienced significant disruptions in their daily lives, including quarantining and interruptions in their education. The drastic changes to these children's environments on their future mental and physical health and development remain unknown.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".