Influenza vaccines licensed in the United States in healthy children: a systematic review and network meta-analysis (Protocol)
Bibliographic record
Abstract
BACKGROUND: Influenza is an acute respiratory illness caused by influenza viruses, which occurs in epidemics worldwide every year. Children are an important target for prevention methods, including vaccination. While evidence about the decision on whether to vaccinate healthy children is robust, evidence supporting the decision of which of available vaccines to use remains unclear.This review will summarize the evidence about the efficacy and safety of the available vaccines for seasonal influenza licensed in the United States for use in healthy children. METHODS/DESIGN: An umbrella systematic review (SR) and network meta-analysis will be conducted of randomized controlled trials (RCTs). We will search for SRs to identify parallel RCTs evaluating inactive and/or live attenuated influenza vaccines licensed in the United States for use in healthy children to prevent influenza. Subsequently, we will update the literature search of the selected SRs to the present time to capture recent controlled studies. To complement the work focused on harms, we will also select observational studies focusing on post marketing retrospective studies. Inclusion will not be limited by language, publication date or publication status. To identify additional candidate studies, we will review the reference lists of the eligible primary studies and narrative reviews; we will query the expert members of the Advisory Committee on Immunization Practices and review references from their previous statement. Additionally, we will review the reports from the Institute of Medicine on the adverse effects of vaccines. Two reviewers will independently determine study eligibility and will extract descriptive, methodological (using the Cochrane risk of bias tool for RCTs and the Newcastle-Ottawa scale for observational studies) and efficacy data. When possible, we will conduct meta-analyses and network meta-analyses by combining indirect and direct comparisons.We will evaluate heterogeneity using the I2 statistic and the agreement of indirect comparisons and direct evidence. We will report the Cochrane Q test to determine the statistical significance of heterogeneity.The overall quality of evidence will be assessed following the GRADE (Grading of Recommendation, Assessment, Development and Evaluation) approach. DISCUSSION: Our systematic review will allow patients, clinicians, guideline developers and policy makers to make evidence-based choices between the two available vaccine options, by providing information regarding benefits and harms of these types of vaccines.
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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 | 0.038 | 0.085 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".