Respiratory syncytial virus-neutralizing serum antibody titers in infants following palivizumab prophylaxis with an abbreviated dosing regimen
Bibliographic record
Abstract
BACKGROUND: Monthly injections of palivizumab during the respiratory syncytial virus (RSV) season in at-risk infants reduces RSV-associated hospitalizations. However, the additive effect of naturally acquired immunity remains unclear. The objective of this study was to assess total neutralizing serum antibodies (NAb) against RSV in at-risk infants who had received an abbreviated course of palivizumab prophylaxis. METHODS: Serum samples were collected from infants enrolled in the RSV Immunoprophylaxis Program in British Columbia, Canada over 2 consecutive RSV seasons (2013 to 2015). Infants in this program had received an abbreviated course of palivizumab in accordance with the provincial guidelines. Data were compared to adults and infants less than 12 months of age who did not receive palivizumab. Anti-RSV NAb titers were measured using an RSV microneutralization assay. FINDINGS: Infants who received palivizumab had anti-RSV NAb titers at the end of the RSV season that persisted beyond what is expected from the pharmacokinetics of palivizumab alone. Moreover, 54% of the control infants who did not receive palivizumab and all tested adults had protective anti-RSV NAb titers. CONCLUSIONS: Based on our observations, we hypothesize that naturally acquired NAb provide additive protection, which may significantly reduce the need for additional doses of palivizumab in infants at risk of severe 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".