Evaluation of vaccine effectiveness of mRNA COVID-19 vaccines in children: a systematic review and meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the vaccine effectiveness (VE) of mRNA COVID-19 vaccines in children using a meta-analysis approach. MATERIALS AND METHODS: Relevant studies on the use of mRNA COVID-19 vaccines in children were identified through computerized searches. VE-related indicators were extracted, and data analysis was performed using the R software with the meta-package. RESULTS: This study included a total of 12 relevant articles involving 9,963,732 participants from multiple centers in different countries, including the United States, Canada, Singapore, Israel, South Korea, and Qatar. The administered vaccine types included BNT162b2 and mRNA-1273. Participants were categorized into partially immunized (one dose of vaccine) and fully immunized (two doses of vaccine). Four articles reported VE after one dose of vaccine, while 12 reported VE after two doses. Heterogeneity analysis indicated significant heterogeneity among the studies, warranting the use of a random-effects model for analysis. Meta-analysis results revealed that the VE of partial immunization ranged from 16.61 (95% CI: 6.32-25.77) to 34.30 (95% CI: 24.21-43.04), with a pooled VE of 22.80 (95% CI: 15.68-29.32). The VE after full immunization ranged from 16.14 (95% CI: 14.42-17.83) to 90.47 (95% CI: 67.42-97.21), with a pooled VE of 56.17 (95% CI: 41.12-67.37). Meta-regression analysis showed no statistically significant correlation between VE and time (p>0.05). CONCLUSIONS: Both partial and full immunization of the BNT162b2 mRNA vaccine provide benefits in reducing infection rates. VE varies over time and is closely associated with viral mutations and waning immunity. The specific mechanisms require further investigation.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.045 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".