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Record W2560446953 · doi:10.1093/infdis/jiw271

Development of Vaccines for Chikungunya Fever

2016· review· en· W2560446953 on OpenAlexaff
Jesse H. Erasmus, Shannan L. Rossi, Scott C. Weaver

Bibliographic record

VenueThe Journal of Infectious Diseases · 2016
Typereview
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of Health
KeywordsChikungunyaVirologyChikungunya feverDiseaseVirusMedicineImmunologyBiology

Abstract

fetched live from OpenAlex

Chikungunya fever, an acute and often chronic arthralgic disease caused by the mosquito-borne chikungunya virus (CHIKV), has reemerged since 2004 to cause millions of cases. Because CHIKV exhibits limited antigenic diversity and is not known to be capable of reinfection, a vaccine could serve to both prevent disease and diminish human amplification during epidemic circulation. Here, we review the many promising vaccine platforms and candidates developed for CHIKV since the 1970s, including several in late preclinical or clinical development. We discuss the advantages and limitations of each, as well as the commercial and regulatory challenges to bringing a vaccine to market.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.334
Teacher spread0.311 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations82
Published2016
Admission routes1
Has abstractyes

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