Off-label use of vaccines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it