Randomized Controlled Trial of Dose Response to Influenza Vaccine in Children Aged 6 to 23 Months
Bibliographic record
Abstract
OBJECTIVES: We assessed whether 2 full versus 2 half-doses of trivalent inactivated influenza vaccine (TIV) could improve immunogenicity without increasing reactogenicity in infants (aged 6-11 months) and toddlers (aged 12-23 months). METHODS: Previously unimmunized infants and toddlers were separately randomly assigned to receive 2 full (0.5-mL) or 2 half (0.25-mL) doses of 2008-2009 split TIV. Sera were collected at enrollment and at 27 to 45 days after the second injection. Parents recorded adverse events after each injection. The primary immunogenicity outcome was superiority (1-sided, α = 0.025) of the full versus the half-dose based on a >10% increase in rates of seroprotection (hemagglutination inhibition titer of ≥40). The primary reactogenicity outcome was fever of ≥38°C within 3 days of either injection. RESULTS: In per-protocol analyses, 252 participants (full dose: n = 124; half-dose: n = 128) were included. In toddlers, postimmunization seroprotection rates exceeded 85% for all 3 vaccine components without significant difference by dose. In infants, the full dose induced higher responses for all 3 vaccine components, meeting the 10% test of superiority for the H3N2 (75.4% vs 47.6%; Δ = 27.8% [95% confidence interval (CI): 11.2-44.5]; P = .02) and B/Yamagata (70.2% vs 41.3%; Δ = 28.9% [95% CI: 11.9-45.9]; P = .02) components but not H1N1 (71.9% vs 54.0%; Δ = 18.0% [95% CI: 1.0-34.9]; P = .2). Rates of fever were not increased among full- versus half-dose recipients in either age group (5.6% vs 12.7% combined). CONCLUSIONS: Administration of 2 full TIV doses may improve immunogenicity without increasing reactogenicity in infants. Current TIV dosing recommendations for young children warrant additional evaluation.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".