MétaCan
Menu
Back to cohort
Record W2912216605 · doi:10.1093/ofid/ofz046

The Cytokine Response Profile of Ebola Virus Disease in a Large Cohort of Rhesus Macaques Treated With Monoclonal Antibodies

2019· article· en· W2912216605 on OpenAlexafffund
Logan Banadyga, Vinayakumar Siragam, Wenjun Zhu, Shihua He, Keding Cheng, Xiangguo Qiu

Bibliographic record

VenueOpen Forum Infectious Diseases · 2019
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsMedicineEbola virusMonoclonal antibodyVirologyEbolavirusAntibodyCohortImmunologyVirusInternal medicine

Abstract

fetched live from OpenAlex

Ebola virus (EBOV) is a highly pathogenic filovirus that causes outbreaks of a severe hemorrhagic fever known as EBOV disease (EVD). Ebola virus disease is characterized in part by a dysregulated immune response and massive production of both pro- and anti-inflammatory cytokines. To better understand the immune response elicited by EVD in the context of treatment with experimental anti-EBOV antibody cocktails, we analyzed 29 cytokines in 42 EBOV-infected rhesus macaques. In comparison to the surviving treated animals, which exhibited minimal aberrations in only a few cytokine levels, nonsurviving animals exhibited a dramatically upregulated inflammatory response that was delayed by antibody treatment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.309
Teacher spread0.299 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations11
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueOpen Forum Infectious DiseasesSame topicViral Infections and Outbreaks ResearchFrench-language works237,207