Long-Term Effect of COVID-19 on Lung Imaging and Function, Cardiorespiratory Symptoms, Fatigue, Exercise Capacity, and Functional Capacity in Children and Adolescents: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The long-term sequela of COVID-19 on young people is still unknown. This systematic review explored the effect of COVID-19 on lung imaging and function, cardiorespiratory symptoms, fatigue, exercise capacity and functional capacity in children and adolescents ≥ 3 months after infection. Methods: A systemic search was completed in the electronic databases of PubMed, Web of Science and Ovid MEDLINE on 27 May 2022. Data on the proportion of participants who had long-term effects were collected, and one-group meta-analysis were used to estimate the pooled prevalence of the outcomes studied. Results: 17 articles met the inclusion criteria, presented data on 124,568 children and adolescents. The pooled prevalence of abnormalities in lung imaging was 10% (95% CI 1–19, I2 = 73%), abnormal pulmonary function was 24% (95% CI 4–43, I2 = 90%), chest pain/tightness was 6% (95% CI 3–8, I2 = 100%), heart rhythm disturbances/palpitations was 6% (95% CI 4–7, I2 = 98%), dyspnea/breathing problems was 16% (95% CI 14–19, I2 = 99%), and fatigue was 24% (95% CI 20–27, I2 = 100%). Decreased exercise capacity and functional limitations were found in 20% (95% CI 4–37, I2 = 88%) and 48% (95% CI 25–70, I2 = 91%) of the participants studied, respectively. Conclusion: Children and adolescents may have persistent abnormalities in lung imaging and function, cardiorespiratory symptoms, fatigue, and decreased functional capacity between 3 to 12 months after infection. More research is needed to understand the long-term effect of COVID-19 on young people, and to clarify its causes and effective management.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".