Nosocomial Rotavirus Infections: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Nosocomial rotavirus (nRV) infections represent an important part of rotavirus (RV)-associated morbidity. The incidence of nRV influences the estimated total RV disease burden, an important determinant of cost-effectiveness of RV vaccination programs. Our aim is to summarize the existing evidence and produce reliable estimates of nRV incidence, in pediatric settings in Europe and North America. METHODS: We searched electronic databases for studies on nRV incidence among pediatric inpatients. To ascertain complete case reporting, only studies describing active nRV surveillance in their methodology were included. Random effects meta-analysis was performed. Meta-regression was used to obtain results adjusted for important study characteristics. RESULTS: Twenty surveillance studies met the quality criteria for inclusion. The pooled unadjusted nRV incidence was 2.9 per 100 hospitalizations (95% confidence interval [CI]: 1.6-4.4). Incidence was significantly influenced by studies' seasonality-months (RV epidemic season only or year-round) and the age range of included patients. Highest nRV incidence was found for children <2 years of age, hospitalized during the epidemic months (8.1/100 hospitalizations; 95% CI: 6.4-9.9). The adjusted year-round nRV incidence estimate without age restriction was 0.4/100 hospitalizations (95% CI: 0.1-2.1) and 0.7 (95% CI: 0.0-1.8) for children <5 years of age. CONCLUSIONS: This is the first meta-analysis to summarize results of surveillance studies on nRV incidence. nRV is an important problem among hospitalized infants during the winter months. The lower season and age-adjusted nRV incidence estimate seems more appropriate for application in population-based burden of disease analysis.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.069 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".