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Record W2602958151 · doi:10.1016/j.vaccine.2017.02.056

Off-label use of vaccines

2017· review· en· W2602958151 on OpenAlexaff
Pieter Neels, James F. Southern, Jon S. Abramson, Philippe Duclos, Joachim Hombach, Melanie Marti, Alanna Fitzgerald-Husek, Jacqueline Fournier-Caruana, Germaine Hanquet

Bibliographic record

VenueVaccine · 2017
Typereview
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersWorld Health Organization
KeywordsLicensurePublic healthVaccinationBusinessMedicinePopulationConfusionEnvironmental healthMarketingPublic relationsMedical educationPolitical scienceImmunologyPsychologyNursing

Abstract

fetched live from OpenAlex

This article reviews the off-label recommendations and use of vaccines, and focuses on the differences between the labelled instructions on how to use the vaccine as approved by the regulatory authorities (or “label” 1 Label: The term "label" means a display of written, printed, or graphic matter upon the immediate container of any article. This includes the Summary of the approved Product Characteristics (SmPC) and Package Insert. 1 ), and the recommendations for use issued by public health advisory bodies at national and international levels. Differences between public health recommendations and the product label regarding the vaccine use can lead to confusion at the level of vaccinators and vaccinees and possibly result in lower compliance with national vaccination schedules. In particular, in many countries, the label may contain regulatory restrictions and warnings against vaccination of specific population groups (e.g. pregnant women) due to a lack of evidence of safety from controlled trials at the time of initial licensure of the vaccine, while public health authorities may recommend the same vaccine for that group, based on additional post-marketing data and benefit risk analyses. We provide an overview of the different responsibilities between regulatory authorities and public health advisory bodies, and the rationale for off-label use 2 Off-label use: Any use of an authorised product not covered by the terms of its marketing authorisation and therefore not in accordance with the SmPC, labelling. 2 of vaccines, the challenges involved based on the impact of off-label use in real-life. We propose to reduce off-label use of vaccines by requiring the manufacturer to regularly adapt the label as much as possible to the public health needs as supported by new evidence. This would require manufacturers to collect and report post-marketing data, communicate them to all stakeholders and regulators to extrapolate existing evidence (when acceptable) to other groups or to other brands of a vaccine (class effect 3 Class effect: An effect for a group of drugs with similar chemical structure and/or drugs with similar mechanism of action and/or drugs with similar pharmacological effects. 3 ). Regulatory authorities have a key role to play by requesting additional post-marketing data, e.g. in specific target groups. When public health recommendations for vaccine use that are outside labelled indications are considered necessary, good communication between regulatory bodies, public health authorities, companies and health care providers or vaccinators is crucial. Recommendations as well as labels and label changes should be evidence-based. The rationale for the discrepancy and the recommended off-label use of a vaccine should be communicated to providers.

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 imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0240.015

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.

Opus teacher head0.453
GPT teacher head0.516
Teacher spread0.063 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations29
Published2017
Admission routes1
Has abstractyes

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