Genomic Loads and Genotypes of Respiratory Syncytial Virus: Viral Factors during Lower Respiratory Tract Infection in Chilean Hospitalized Infants
Bibliographic record
Abstract
The clinical impact of viral factors (types and viral loads) during respiratory syncytial virus (RSV) infection is still controversial, especially regarding newly described genotypes. In this study, infants with RSV bronchiolitis were recruited to describe the association of these viral factors with severity of infection. RSV antigenic types, genotypes, and viral loads were determined from hospitalized patients at Hospital Roberto del Río, Santiago, Chile. Cases were characterized by demographic and clinical information, including days of lower respiratory symptoms and severity. A total of 86 patients were included: 49 moderate and 37 severe cases. During 2013, RSV-A was dominant (86%). RSV-B predominated in 2014 (92%). Phylogenetic analyses revealed circulation of GA2, Buenos Aires (BA), and Ontario (ON) genotypes. No association was observed between severity of infection and RSV group (p = 0.69) or genotype (p = 0.87). After a clinical categorization of duration of illness, higher RSV genomic loads were detected in infants evaluated earlier in their disease (p < 0.001) and also in infants evaluated later, but coursing a more severe infection (p = 0.04). Although types and genotypes did not associate with severity in our children, higher RSV genomic loads and delayed viral clearance in severe patients define a group that might benefit from new antiviral therapies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".