Prevención de la infección por virus respiratorio sincicial en lactantes. ¿Qué se ha hecho y en qué estamos hoy?
Bibliographic record
Abstract
Respiratory syncytial virus (RSV) infection is a frequent cause of morbidity and mortality in children. Recently, great advances have been made in the development of new monoclonal antibodies and vaccines thanks to the recognition of the structural conformation of virus proteins. The objective of this study was to review the advances related to the prevention of RSV infection in the first 6 months of life. Advances in structural biology have shown that the RSV fusion protein (F-Protein) in its prefusion state (Pre-F) is an excellent antigen for developing monoclonal antibodies and vaccines to prevent respiratory syncytial virus (RSV) infections. A new single-dose monoclonal antibody, Nirsevimab, has greater neutralizing power than currently available Palivizumab, and prolonged protection for 5 to 6 months. Nirsevimab has demonstrated an efficacy of 76.8% (95% CI, 49.4 to 89.4) in preventing lower respiratory infection 150 days after vaccination, decreasing the risk of ICU admission by 90.1% (95% CI: 16.4-98.8). Clesrovimab is another single-dose monoclonal antibody that has also shown promising results in phase 1b-2a trials. More recently, a bivalent vaccine against RSV A and B (Bivalent Prefusion F) has also been developed by replicating the F-protein stabilized in its Pre-F state as an antigen, using genetic engineering. This antigen, when administered to pregnant women between 24-36 weeks of gestation, induces high levels of antibodies in the mother with high transplacental transfer to the fetus. This vaccine has demonstrated an efficacy of 81.8% (95% CI: 40.6-96.3) at 90 days and 69.4% (95% CI: 44.3-84.1) at 180 days to prevent severe RSV disease (primary endpoint) without safety events detected so far. Nirsevimab and the Pre-F vaccine for pregnant women confer effective protection through passive immunity against RSV that lasts for the first 5 to 6 months of life and have already been approved for use in Europe by the EMA and in Canada and the United States by the FDA.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".