Evaluation of the decision-making process underlying the initial off-label use of vaccines: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Vaccination has become a central part of public health prevention. Vaccines are introduced after licensure by national regulatory authorities, whereas recommendations for use of licensed vaccines are made by national or international advisory committees and may include off-label use. The methodological and decision-making processes that are used to assess novel initial off-label vaccine use are unclear. This review aims to examine the off-label assessment processes to map evidence and concepts used in the decision-making process and present a common approach between all recommendations and specifics of each decision. METHODS AND ANALYSIS: The methodological framework described at the Joanna Briggs Institute will be applied to this scoping review. A search strategy was developed, in collaboration with an experienced senior health research librarian, by combining Mesgarpour's highly sensitive search strategies. Peer-reviewed and grey literature will be systematically identified using PubMed, Medline and EMBASE; governmental agency and pharmaceutical websites; and search engines, such as Google Scholar. Reports and studies on off-label vaccine use in public health will be included. Screening will be independently undertaken by two reviewers. Data will be extracted using a standard form. Results will be narratively summarised to highlight relevant findings and guide the development of an analytical framework for off-label vaccination recommendations. ETHICS AND DISSEMINATION: This research does not require ethical approval. This scoping review will provide decision-making elements and a synthesis of knowledge on vaccines off-label use. Findings will be relevant to decision-makers/advisory committees and public health. These will be disseminated through peer-reviewed articles and conferences.
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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 | 0.295 | 0.252 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.018 |
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".