Approach to prevention of respiratory syncytial virus disease in infants by passive immunization
Bibliographic record
Abstract
OBJECTIVE: To support family physicians in discussing respiratory syncytial virus (RSV) immunizations with patients. SOURCES OF INFORMATION: Information was obtained through a literature review on the burden of RSV disease in infants; observational studies; randomized controlled trials; evidence presented by review agencies; product monographs; and National Advisory Committee on Immunization statements. MAIN MESSAGE: There are now 3 options available for preventing severe RSV disease in infants: the monoclonal antibody palivizumab, the long-acting monoclonal antibody nirsevimab, and the new RSVpreF vaccine administered during pregnancy. Only a small number of infants at high risk of severe RSV disease are eligible for palivizumab. Nirsevimab has received Health Canada authorization for all infants and RSVpreF has received authorization for all pregnant women and pregnant people. There are multiple considerations for the use of each product, including authorization; availability; timing of administration; health status and social determinants of health of the infant; efficacy and effectiveness; safety; patient preference; and cost. The National Advisory Committee on Immunization has recently issued guidance on the use of RSV immunization products for infants. CONCLUSION: Family doctors should be aware of the indications, relative benefits, and recommendations for the 3 RSV immunization products to have informed discussions with patients, taking into account the preferences and circumstances of the pregnant woman or pregnant person or of the parent and infant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".