Systematic Review and Expert Consensus on the Use of Long-acting Monoclonal Antibodies for Prevention of Respiratory Syncytial Virus Disease: ARMADA (Advancing RSV Management And Disease Awareness) Taskforce
Bibliographic record
Abstract
Background: Long-acting monoclonal antibodies (LAmAbs) could dramatically reduce the respiratory syncytial virus (RSV) disease burden in children if implemented using clear, evidence-based recommendations. Methods: The ARMADA Taskforce-an international, multidisciplinary expert panel-undertook a systematic review to develop LAmAbs consensus recommendations for RSV disease prevention in children. Results: The Taskforce recommends LAmAbs for all infants aged <8 months in the absence of maternal RSV vaccination, preterm infants (<37 weeks' gestational age) aged <12 months, and children <24 months with high-risk conditions. Seasonal LAmAb administration is recommended, although in RSV-endemic countries decisions should be made locally concerning administration year-round or with peak RSV incidences. Conclusions: The Taskforce strongly endorses LAmAbs implementation based on their efficacy, effectiveness, and public health impact. These recommendations provide a blueprint to inform guidelines worldwide. Wider equitable access to LAmAbs at affordable prices, especially in low- and middle-income countries is needed to reduce the childhood RSV burden.
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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 | 0.029 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".