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Record W2519857492 · doi:10.1002/rmv.1903

Lujo viral hemorrhagic fever: considering diagnostic capacity and preparedness in the wake of recent Ebola and Zika virus outbreaks

2016· review· en· W2519857492 on OpenAlexaff
Edgar Simulundu, Aaron S. Mweene, Katendi Changula, Mwaka Monze, Elizabeth Chizema, Peter Mwaba, Ayato Takada, Giuseppe Ippolito, Francis Kasolo, Alimuddin Zumla, Matthew Bates

Bibliographic record

VenueReviews in Medical Virology · 2016
Typereview
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsInstitute of Infection and Immunity
FundersWorld Health Organization
KeywordsOutbreakViral hemorrhagic feverVirologyEbola virusArenavirusCase fatality rateZika virusPreparednessTransmission (telecommunications)MedicineDiseaseVirusImmunologyEnvironmental healthLymphocytic choriomeningitisPopulationPathology

Abstract

fetched live from OpenAlex

Lujo virus is a novel Old World arenavirus identified in Southern Africa in 2008 as the cause of a viral hemorrhagic fever (VHF) characterized by nosocomial transmission with a high case fatality rate of 80% (4/5 cases). Whereas this outbreak was limited, the unprecedented Ebola virus disease outbreak in West Africa, and recent Zika virus disease epidemic in the Americas, has brought into acute focus the need for preparedness to respond to rare but potentially highly pathogenic outbreaks of zoonotic or arthropod-borne viral infections. A key determinant for effective control of a VHF outbreak is the time between primary infection and diagnosis of the index case. Here, we review the Lujo VHF outbreak of 2008 and discuss how preparatory measures with respect to developing diagnostic capacity might be effectively embedded into existing national disease control networks, such as those for human immunodeficiency virus, tuberculosis, and malaria.

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)0.002
metaresearch head score (Gemma)0.003
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: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.103
GPT teacher head0.409
Teacher spread0.305 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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