Characteristics and methodological standards across systematic reviews with Meta-analysis of efficacy and/or effectiveness of influenza vaccines: an overview of reviews
Bibliographic record
Abstract
While systematic reviews (SR) generally suggest that vaccination is an effective way to prevent influenza infection, it is not clear if these conclusions are based on high quality SR methods. As such, we systematically identified, critically appraised, and summarised the characteristics and adherence to methodological standards in SRs with meta-analysis of efficacy/effectiveness of influenza vaccines. We searched MEDLINE, Embase, Scopus, CINAHL, Global Health, and CDSR for English-language SR publications up to July 11, 2022. We summarised the characteristics, adherence to methodological standards and SR quality (AMSTAR 2). From 11,193 retrieved citations, we included 48 publications (47 SRs). Seventy-five percent were of a critically low quality, 19% of a low quality, 2% of a moderate quality, and 4% of a high quality. Thirteen percent were industry-funded, about 13% co-authored by industry employee(s), and 4% commissioned by an organisation or authority. Only 45% percent reported protocol registration, 6% reported collaboration with a knowledge synthesis librarian/information specialist, and 60% utilised a reporting checklist (e.g. PRISMA). SRs with meta-analysis of efficacy/effectiveness of influenza vaccines are mostly of critically low quality and even the more recent reviews did not follow current best SR practices. These findings are significant in view of the controversies that surround influenza vaccines, and the use of SRs in informed decision-making. However, the findings do not justify curtailment or cessation of influenza vaccine use as vaccines continue to offer substantial net public health benefit.HighlightsWe systematically identified, critically appraised, and summarised the characteristics and adherence to methodological standards in 47 systematic reviews with meta-analysis of efficacy/effectiveness of influenza vaccines.13% of the reviews were industry-funded.About 13% of the reviews were co-authored by industry employee(s).4% of the reviews were commissioned by an organisation/authority.45% of the reviews reported protocol registration.6% of the reviews reported collaborating with a knowledge synthesis librarian/information specialist to prepare the search strategy.60% of the reviews reported using the PRISMA (or similar) checklist.75% of the reviews were judged to be of critically low quality; 19% of low quality; 2% of moderate quality; 4% of high quality. We systematically identified, critically appraised, and summarised the characteristics and adherence to methodological standards in 47 systematic reviews with meta-analysis of efficacy/effectiveness of influenza vaccines. 13% of the reviews were industry-funded. About 13% of the reviews were co-authored by industry employee(s). 4% of the reviews were commissioned by an organisation/authority. 45% of the reviews reported protocol registration. 6% of the reviews reported collaborating with a knowledge synthesis librarian/information specialist to prepare the search strategy. 60% of the reviews reported using the PRISMA (or similar) checklist. 75% of the reviews were judged to be of critically low quality; 19% of low quality; 2% of moderate quality; 4% of high quality.
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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 | 0.432 | 0.750 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.042 | 0.052 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".