Pediatrician’s role in vaccinating children and families for COVID-19: no one left behind
Bibliographic record
Abstract
The importance of coronavirus disease 2019 (COVID-19) vaccines in children has been debated during the pandemic because the incidence of COVID-19 in children is lower than in adults, with particularly low rates in children <5 years of age. 1 However, the physical and mental health of children has been greatly impacted by both direct and indirect effects of the COVID-19 pandemic. More than six million children have been diagnosed with COVID-19 in the United States alone 1 , over 4,000 children have been hospitalized 2 and over 600 children have died. 1 Globally, there have been over ten million COVID-19 cases and over 4000 deaths in persons 19 years of age and younger. 3 The number of pediatric COVID-19 cases may be underestimated because children tend to have milder symptoms from infection and may be less likely to be tested than adults, particularly in low- and middle-income countries where access to testing may be limited. and COVID-19 case data are not always reported by age group. Children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are at risk of postinfectious complications including Multi-system Inflammatory Syndrome in Children 4 and “long-COVID.” 5 Pandemic mitigation measures such as school closures and cancellation of athletic activities have been associated with increased mental health difficulties and obesity rates in children, and have widened health disparities related to race/ethnicity and socioeconomic status. 6 Beyond COVID-19 infection, the impact of the pandemic on children’s mental and physical wellbeing and educational progress has been far-reaching. 7
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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 | 0.032 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.015 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.165 | 0.138 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".