Nirsevimab in the prevention of respiratory syncytial virus lower respiratory tract disease: a profile of its use
Bibliographic record
Abstract
Declarations Funding The preparation of this review was not supported by any external funding. Authorship and conflict of interest M. Shirley is a salaried employee of Adis International Ltd/Springer Nature and declare(s) no relevant conflicts of interest. All authors contributed to this article and are responsible for its content. Ethics approval, Consent to participate, Consent for publication, Availability of data and material, Code availability Not applicable. Additional information about this Adis Drug Review can be found here. Abstract Nirsevimab (Beyfortus™), a long-acting monoclonal antibody targeting the respiratory syncytial virus (RSV) fusion (F) protein, is the first prophylactic monoclonal antibody against RSV to be licensed for use in all infants in their first RSV season, and thus presents a highly valuable tool in the fight against RSV disease in children. Additionally, in the USA and Canada, nirsevimab is licensed for use in children up to 24 months of age who remain vulnerable to severe RSV disease through their second RSV season. Data from randomized, double-blind, placebo-controlled clinical trials show that a single intramuscular dose of nirsevimab is efficacious in reducing the incidence of medically attended RSV lower respiratory tract (LRT) disease in healthy term and preterm infants through at least 150 days in their first RSV season. Pharmacokinetic data also support the efficacy of nirsevimab in infants at higher risk of severe RSV disease, including in the second RSV season with a second nirsevimab dose. Nirsevimab has an extended serum half-life, resulting in a duration of protection from a single dose which can cover a full typical RSV season. Nirsevimab is well tolerated, with low rates of reactogenicity, and can be administered concomitantly with childhood vaccines. © Springer Nature Switzerland AG 2023
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".