Respiratory Syncytial Virus: A WAidid Consensus Document on New Preventive Options
Bibliographic record
Abstract
Background/Objectives: Respiratory syncytial virus (RSV) is a leading cause of respiratory infections, particularly affecting young infants, older adults, and individuals with comorbidities. Methods: This document, developed as a consensus by an international group of experts affiliated with the World Association of Infectious Diseases and Immunological Disorders (WAidid), focuses on recent advancements in RSV prevention, highlighting the introduction of monoclonal antibodies (mAbs) and vaccines. Results: Historically, RSV treatment options were limited to supportive care and the monoclonal antibody palivizumab, which required multiple doses. Recent innovations have led to the development of long-acting mAbs, such as nirsevimab, which provide season-long protection with a single dose. Nirsevimab has shown high efficacy in preventing severe RSV-related lower respiratory tract infections (LRTIs) in infants, reducing hospitalizations and ICU admissions. Additionally, new vaccines, such as RSVpreF and RSVpreF3, target older adults and have demonstrated significant efficacy in preventing LRTIs in clinical trials. Maternal vaccination strategies also show promise in providing passive immunity to newborns, protecting them during the most vulnerable early months of life. This document further discusses the global burden of RSV, its economic impact, and the challenges of implementing these preventative strategies in different healthcare settings. Conclusions: The evidence supports the integration of both passive (mAbs) and active (vaccines) immunization approaches as effective tools to mitigate the public health impact of RSV. The combined use of these interventions could substantially reduce RSV-related morbidity and mortality across various age groups and populations, emphasizing the importance of widespread immunization efforts.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".