The Clinical Utility of Respiratory Viral Testing in Hospitalized Children: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Respiratory virus (RV) detection tests are commonly used in hospitalized children to diagnose viral acute respiratory infection (ARI), but their clinical utility is uncertain. OBJECTIVES: To systematically review and meta-analyze the impact of RV test results on antibiotic consumption, ancillary testing, hospital length of stay, and antiviral use in children hospitalized with severe ARI. DATA SOURCES: Seven medical literature databases from 1985 through January 2018 were analyzed. STUDY SELECTION: Studies in children <18 years old hospitalized for severe ARI in which the clinical impact of a positive versus negative RV test result or RV testing versus no testing are compared. DATA EXTRACTION: Two reviewers independently screened titles, abstracts, and full texts; extracted data; and assessed study quality. RESULTS: We included 23 studies. High heterogeneity did not permit an overall meta-analysis. Subgroup analyses by age, RV test type, and viral target showed no difference in the proportion of patients receiving antibiotics between those with positive versus negative test results. Stratification by study design revealed that RV testing decreased antibiotic use in prospective cohort studies (odds ratio = 0.58; 95% confidence interval: 0.45-0.75). Pooled results revealed no conclusive impact on chest radiograph use (odds ratio = 0.71; 95% confidence interval: 0.48-1.04). Results of most studies found that positive RV test results did not impact median hospital length of stay, but they may decrease antibiotic duration. Nineteen (83%) studies were at serious risk of bias. LIMITATIONS: Low-quality studies and high clinical and statistical heterogeneity were among the limitations. CONCLUSIONS: Higher-quality prospective studies are needed to determine the impact of RV testing on antibiotic use in children hospitalized with severe ARI.
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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 | 0.024 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.058 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".