Efficacy and Safety of Palivizumab as a Prophylaxis for Respiratory Syncytial Virus (RSV) Disease: An Updated Systemic Review and Meta-Analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to summarize the current evidence regarding the efficacy and safety of palivizumab as a prophylaxis for respiratory syncytial virus (RSV) disease. We searched MEDLINE via PubMed, Scopus, Cochrane, Web of Science, Embase, and Science Direct from inception till November 2023. Studies that assessed the efficacy and safety of palivizumab in infants aged between 28 days and three months of age were included. We analyzed the data using Review Manager 5.4 software, with results pooled across studies and expressed as risk ratios (RR) with 95% confidence intervals (CI). A total of 10 studies were included. The effect estimates favored palivizumab over placebo regarding the hospitalization for RSV infection (RR=0.51, 95% CI: 0.40 to 0.65; P<0.00001) and ICU admission (RR=0.49, 95% CI: 0.30 to 0.81; P=0.005). On the other hand, the effect estimate showed no significant difference between palivizumab and placebo regarding all-cause mortality (RR=0.69, 95% CI: 0.42 to 1.15; P=0.16), lower respiratory tract infection (RR=0.42, 95% CI: 0.11 to 1.69; P=0.22), and need for mechanical ventilation (RR=0.75, 95% CI: 0.34 to 1.67; P=0.48). Palivizumab can be considered a prophylaxis for RSV disease in young children as it is safe, well-tolerated, and effective in reducing RSV hospitalizations. However, further research through high-quality randomized controlled trials is required to determine its efficacy as a therapeutic agent for established RSV infections.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".