Who is at a Higher Risk? A brief review of Recent Evidence on comorbidities in children infected with COVID-19.
Bibliographic record
Abstract
BACKGROUND: COVID-19 has affected both adults and children with variable presentations and disease severity. Children can present with mild symptoms of fever, cough and shortness of breath, and rapidly progress to severe pneumonia, requiring mechanical ventilation. This population includes children who are younger than one year and older adolescents who have an underlying comorbidity-specifically immunosuppression or prior cardio-respiratory infections. In this review, we discuss the determinants of severe disease among the paediatric patients- primarily asthma, immune-status, obesity and multisystem inflammatory syndrome in children (MIS-C). Asthma and underlying lung pathologies can be a strong predictor (~20% prevalence) for development of severe COVID-19 infection, irrespective of age. However, as compared to asthma, a higher mortality rate was reported in immune-compromised patients. With a weakened immune system, immunosuppressed individuals were 1.55 times and immunocompromised patients 3.29 times more vulnerable to developing severer COVID-19 disease. Similarly, evidence suggests that a BMI of greater than 35 kg/m2 renders individuals more susceptible to developing COVID-19-related complications. This observation is based on the negative impacts obesity has on pulmonary functions and in downplaying the immune system. Furthermore, a possible association of COVID-19 and MIS-C has been reported by multiple studies across the globe but it needs further studies to strengthen its stance due to the scarcity of data when compared with the other determinants discussed in this article. Authors recommend researchers directing attention on synthesizing the evolving evidence to fill the knowledge void in the paediatric population, which will better enable paediatricians to make informed decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".