Respiratory Viral Co-infection in SARS-CoV-2-Infected Children During the Early and Late Pandemic Periods
Bibliographic record
Abstract
BACKGROUND: Knowledge regarding the impact of respiratory pathogen co-infection in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected children seeking emergency department care is limited, specifically as it relates to the association between SARS-CoV-2 viral co-infection and disease severity and factors associated with co-infection. METHODS: This secondary analysis included data from 2 prospective cohort studies conducted between March 2020 and February 2022 that included children <18 years of age tested for SARS-CoV-2 infection along with additional respiratory viruses in a participating emergency department. Outcomes included the detection rate of other respiratory viruses and the occurrence of severe outcomes (ie, intensive interventions, severe organ impairment and death). RESULTS: We included 2520 participants, of whom 388 (15.4%) were SARS-CoV-2-positive. Detection of additional respiratory viruses occurred in 18.3% (71/388) of SARS-CoV-2-positive children, with rhinovirus/enterovirus being most frequently detected (42/388; 10.8%). In multivariable analyses (adjusted odds ratio and 95% confidence interval), among SARS-CoV-2-positive children, detection of another respiratory virus was not associated with severe outcomes [1.74 (0.80-3.79)], but detection of rhinovirus/enterovirus [vs. isolated SARS-CoV-2 detection 3.56 (1.49-8.51)] and having any preexisting chronic medical condition [2.15 (1.06-4.36)] were associated with severe outcomes. Among SARS-CoV-2-positive children, characteristics independently associated with an increased odds of any other viral co-infection included: age and delta variant infection. CONCLUSIONS: Approximately 1 in 5 children infected with SARS-CoV-2 had co-infection with another respiratory virus, and co-infection with rhinovirus/enterovirus was associated with severe outcomes. When public health restrictions were relaxed, co-infections increased.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".