Use of existing systematic reviews for the development of evidence-based vaccination recommendations: Guidance from the SYSVAC expert panel
Bibliographic record
Abstract
National immunization technical advisory groups (NITAGs) develop immunization-related recommendations and assist policy-makers in making evidence informed decisions. Systematic reviews (SRs) that summarize the available evidence on a specific topic are a valuable source of evidence in the development of such recommendations. However, conducting SRs requires significant human, time, and financial resources, which many NITAGs lack. Given that SRs already exist for many immunization-related topics, and to prevent duplication and overlap of reviews, a more practical approach may be for NITAGs to use existing SRs. Nevertheless, it can be challenging to identify relevant SRs, to select one SR from among multiple SRs, or to critically assess and effectively use them. To support NITAGs, the London School of Hygiene and Tropical Medicine, Robert Koch Institute and collaborators developed the SYSVAC project, which consists of an online registry of systematic reviews on immunization-related topics and an e-learning course, that supports the use of them (both freely accessible at https://www.nitag-resource.org/sysvac-systematic-reviews). Drawing from the e-learning course and recommendations from an expert panel, this paper outlines methods for using existing systematic reviews when making immunization-related recommendations. With specific examples and reference to the SYSVAC registry and other resources, it offers guidance on locating existing systematic reviews; assessing their relevance to a research question, up-to-dateness, and methodological quality and/or risk of bias; and considering the transferability and applicability of their findings to other populations or settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.437 | 0.608 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.041 | 0.026 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.016 | 0.017 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".