The Evaluation of Health Care Services for Children and Adolescents With Post–COVID-19 Condition: Protocol for a Prospective Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: Some children and adolescents suffer from late effects of a SARS-CoV-2 infection despite a frequently mild course of the disease. Nevertheless, extensive care for post-COVID-19 condition, also known as post-COVID-19 syndrome, in children and young people is not yet available. A comprehensive care network, Post-COVID Kids Bavaria (PoCo), for children and adolescents with post-COVID-19 condition has been set up as a model project in Bavaria, Germany. OBJECTIVE: The aim of this study is to evaluate the health care services provided within this network structure of care for children and adolescents with post-COVID-19 condition in a pre-post study design. METHODS: We have already recruited 117 children and adolescents aged up to 17 years with post-COVID-19 condition who were diagnosed and treated in 16 participating outpatient clinics. Health care use, treatment satisfaction, patient-reported outcomes related to health-related quality of life (the primary endpoint), fatigue, postexertional malaise, and mental health are being assessed at different time points (at baseline and after 4 weeks, 3 months, and 6 months) using routine data, interviews, and self-report questionnaires. RESULTS: The study recruitment process ran from April 2022 until December 2022. Interim analyses will be carried out. A full analysis of the data will be conducted after follow-up assessment is completed, and the results will be published. CONCLUSIONS: The results will contribute to the evaluation of therapeutic services provided for post-COVID-19 condition in children and adolescents, and avenues for optimizing care may be identified. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41010.
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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 | 0.044 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".