Initial Effectiveness of mRNA-1273 Against SARS-CoV-2 Infection and Hospitalization in Young Children
Bibliographic record
Abstract
Background: Data on mRNA-1273 (Moderna) vaccine effectiveness (VE) in children aged 6 months to 5 years are limited. The objectives of this study were to assess mRNA-1273 vaccine effectiveness against symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19)-related hospitalization among children aged 6 months to 5 years during the initial 5 months of the vaccination campaign rollout, as well as to determine whether VE varied by age group (6 months to <2 years vs 2 to 5 years). Methods: We used a test-negative study with linked health administrative data in Ontario, Canada, to evaluate vaccine effectiveness of mRNA-1273 against symptomatic SARS-CoV-2 infection and COVID-19-related hospitalization from July 28 to December 31, 2022. Participants included symptomatic children aged 6 months to 5 years who were tested by real-time polymerase chain reaction. The primary outcome was symptomatic infection, and the secondary outcome was COVID-19-related hospitalization. Results: We included 572 test-positive cases and 3467 test-negative controls. Receipt of mRNA-1273 was associated with reduced symptomatic SARS-CoV-2 infection (VE, 90%; 95% CI, 53%-99%) and COVID-19-related hospitalization (VE, 82%; 95% CI, 4%-99%) ≥7 days after the second dose. We were unable to detect heterogeneity in VE across age groups. Conclusions: Our findings suggest that mRNA-1273 vaccine effectiveness was initially strong against symptomatic SARS-CoV-2 infection and hospitalization in children aged 6 months to 5 years. Further research is needed to understand long-term effectiveness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".