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Record W2938056705 · doi:10.1111/cei.13284

Vaccines for emerging pathogens: prospects for licensure

2019· review· en· W2938056705 on OpenAlexaboutno aff
E. Diane Williamson, G E Westlake

Bibliographic record

VenueClinical & Experimental Immunology · 2019
Typereview
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsLicensureImmunologyVirologyMedicineBiology

Abstract

fetched live from OpenAlex

Globally, there are a number of emerging pathogens. For most, there are no licensed vaccines available for human use, although there is ongoing research and development. However, given the extensive and increasing list of emerging pathogens and the investment required to bring vaccines into clinical use, the task is huge. Overlaid on this task is the risk of anti-microbial resistance (AMR) acquisition by micro-organisms which can endow a relatively harmless organism with pathogenic potential. Furthermore, climate change also introduces a challenge by causing some of the insect vectors and environmental conditions prevalent in tropical regions to begin to spread out from these traditional areas, thus increasing the risk of migration of zoonotic disease. Vaccination provides a defence against these emerging pathogens. However, vaccines for pathogens which cause severe, but occasional, disease outbreaks in endemic pockets have suffered from a lack of commercial incentive for development to a clinical standard, encompassing Phase III clinical trials for efficacy. An alternative is to develop such vaccines to request US Emergency Use Authorization (EUA), or equivalent status in the United States, Canada and the European Union, making use of a considerable number of regulatory mechanisms that are available prior to licensing. This review covers the status of vaccine development for some of the emerging pathogens, the hurdles that need to be overcome to achieve EUA or an equivalent regional or national status and how these considerations may impact vaccine development for the future, such that a more comprehensive stockpile of promising vaccines can be achieved.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.222
GPT teacher head0.535
Teacher spread0.313 · 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 teacher head, not a consensus.

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

Citations19
Published2019
Admission routes1
Has abstractyes

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