Effectiveness of Palivizumab in High-risk Infants and Children
Bibliographic record
Abstract
BACKGROUND: Infants with premature birth ≤35 weeks gestational age, chronic lung disease of prematurity and congenital heart disease are at an increased risk for lower respiratory tract infections and hospitalization from respiratory syncytial virus (RSV), which has been shown in randomized trials to be prevented by palivizumab. However, palivizumab effectiveness (PE) has not been studied in a large clinical setting. METHODS: A multicenter study among high-risk US and Canadian children younger than 24 months hospitalized with lower respiratory tract infection and whose nasopharyngeal aspirates were tested for human metapneumovirus (HMPV) and RSV were the subjects of the trial. We conducted a test-negative case-control study in these subjects to determine PE. We used an inverse propensity score weighted (IPSW) multiple logistic regression model to adjust PE. RESULTS: Palivizumab was used in 434 (51%) of 849 eligible children. RSV was identified in 403 (47%) children. The unadjusted PE was 43% [95% confidence interval (CI), 34%-51%)]. After IPSW adjustment, the adjusted PE was 58% (95% CI, 43%-69%). Palivizumab prevented intensive care unit admissions (PE, 62%; 95% CI, 35%-78%). PE for 29-35 weeks gestational age and ≤6 months of chronologic age without chronic lung disease of prematurity or congenital heart disease was 74% (95% CI, 56%-85%). CONCLUSIONS: Using a test-negative case-control design with RSV molecular detection, palivizumab is shown to prevent RSV hospitalizations and intensive care unit admissions in high-risk infants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".