Challenges in the prevention or treatment of RSV with emerging new agents in children from low- and middle-income countries
Bibliographic record
Abstract
INTRODUCTION: Respiratory syncytial virus (RSV) causes approximately 120,000 deaths annually in children <5 years, with 99% of fatalities occurring in low- and middle-income countries (LMICs). AREAS COVERED: There are numerous RSV interventions in development, including long-acting monoclonal antibodies, vaccines (maternal and child) and treatments which are expected to become available soon. We reviewed the key challenges and issues that need to be addressed to maximize the impact of these interventions in LMICs. The epidemiology of RSV in LMICs was reviewed (PubMed search to 30 June 2020 inclusive) and the need for more and better-quality data, encompassing hospital admissions, community contacts, and longer-term respiratory morbidity, emphasized. The requirement for an agreed clinical definition of RSV lower respiratory tract infection was proposed. The pros and cons of the new RSV interventions are reviewed from the perspective of LMICs. EXPERT OPINION: We believe that a vaccine (or combination of vaccines, if practicable) is the only viable solution to the burden of RSV in LMICs. A coordinated program, analogous to that with polio, involving governments, non-governmental organizations, the World Health Organization, the manufacturers and the healthcare community is required to realize the full potential of vaccine(s) and end the devastation of RSV in LMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".