The second-order effects that the COVID-19 pandemic has had on pediatric populations
Bibliographic record
Abstract
INTRODUCTION: SARS-CoV-2 can have long-term health consequences that persist beyond acute infection. While this is evident in adults and the elderly, the impact on children and adolescents remains under recognized. Here we navigate the second-order post-acute effects that the COVID-19 has had on the pediatric populations, with the exception of the mental health implication of social restrictions. AREAS COVERED: We outline common scenarios related with SARS-CoV-2 infection encountered in pediatric clinical practice, such as in the Multisystem inflammatory syndrome (MIS-C), Long Covid, neurological and autoimmune complications of Covid-19, immunological impact of the viral infection, as well as epidemiological and public health consequences associated with the implementation of non-pharmacological interventions. EXPERT OPINION: SARS-CoV-2 has had several second-order effects on child health, from a biological, epidemiological, and public health perspective, highlighting the complexity of dealing with new infections and the urgent need to implement multidisciplinary interventions that support the health of people at single person and societal level. Funding on modern surveillance system, preventing strategies and research to better understand and cure post-acute complications of viral infections should be a priority of every funding agency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".