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Ebola: Lessons on Vaccine Development

2018· review· en· W2889769667 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnual Review of Microbiology · 2018
Typereview
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of Manitoba
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of Health
KeywordsEbola vaccineEbola virusLicensureClinical trialImmunogenicityOutbreakMedicineVirologyImmunologyImmune systemPathology

Abstract

fetched live from OpenAlex

The West African Ebola virus (EBOV) epidemic has fast-tracked countermeasures for this rare, emerging zoonotic pathogen. Until 2013-2014, most EBOV vaccine candidates were stalled between the preclinical and clinical milestones on the path to licensure, because of funding problems, lack of interest from pharmaceutical companies, and competing priorities in public health. The unprecedented and devastating epidemic propelled vaccine candidates toward clinical trials that were initiated near the end of the active response to the outbreak. Those trials did not have a major impact on the epidemic but provided invaluable data on vaccine safety, immunogenicity, and, to a limited degree, even efficacy in humans. There are plenty of lessons to learn from these trials, some of which are addressed in this review. Better preparation is essential to executing an effective response to EBOV in the future; yet, the first indications of waning interest are already noticeable.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.759
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.071
GPT teacher head0.439
Teacher spread0.368 · 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