Bibliographic record
Abstract
Coronavirus Disease 2019 (COVID-19) has become notorious for its transmissibility and virulence among adults and the elderly However, it is becoming increasingly clear that children are not spared from the grips of this infectious disease As the six-month anniversary of the pandemic approaches, a notable rise is evident in pediatric COVID-19 cases, particularly severe cases Yet, coronavirus-related research has been concentrated towards older demographics, the result of which is an insufficient understanding of the disease in children This makes it more difficult to manage severe pediatric cases in clinical settings This narrative review presents a summary of COVID-19 literature from a pediatric lens, as it is understood today It consolidates the range of clinical features observed in child-related cases, evaluates the features unique to pediatric patients and explores the unprecedented spike of multisystem inflammatory conditions coinciding with the pandemic Regarding the current understanding of COVID-19 in children, three areas requiring further research were identified First, clinical trials determining the safety and efficacy of remdesivir, and other drug candidates, must be elucidated in pediatric patients A shift towards larger-scale, multicenter case studies are also needed when examining the poorly understood, child-specific COVID-19 features that have been observed Further investigation into these features, which include delayed symptoms, prolonged viral presence, and prevalence of asymptomatic cases, may help in achieving a better understanding of the disease pathogenesis in children Finally, the effectiveness of interventions like aspirin for long-term complications of inflammatory conditions associated with COVID-19, must be established It is imperative to elucidate the pathogenesis of COVID-19 and gain a better understanding of treatment guidelines for children to manage the mounting rates of infection and cases of increased severity observed in this young demographic © 2021, University of Toronto All rights reserved
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".