Evidence map›Paper›PMID 39447210›Full record

ArticleEmerging infectious diseases2024

Rapid Decision Algorithm for Patient Triage during Ebola Outbreaks.

Denis-Luc Ardiet, Justus Nsio, Gaston Komanda, Rebecca M Coulborn, Emmanuel Grellety, Francesco Grandesso, Richard Kitenge, Dolla L Ngwanga, Bibiche Matady, Guyguy Manangama and 6 more

Abstract read
In one paragraph

Article in Emerging infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Denis-Luc Ardiet
Justus Nsio
Gaston Komanda
Rebecca M Coulborn
Emmanuel Grellety
Francesco Grandesso
Richard Kitenge
Dolla L Ngwanga
Bibiche Matady
Guyguy Manangama
Mathias Mossoko
John K Ngwama
Placide Mbala
Francisco Luquero
Klaudia Porten
Steve Ahuka-Mundeke

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The low specificity of Ebola virus disease clinical signs increases the risk for nosocomial transmission to patients and healthcare workers during outbreaks. Reducing this risk requires identifying patients with a high likelihood of Ebola virus infection. Analyses of retrospective data from patients suspected of having Ebola virus infection identified 13 strong predictors and time from disease onset as constituents of a prediction score for Ebola virus disease. We also noted 4 highly predictive variables that could distinguish patients at high risk for infection, independent of their scores. External validation of this algorithm on retrospective data revealed the probability of infection continuously increased with the score.

Indexed as

AlgorithmsDisease OutbreaksHemorrhagic Fever, EbolaTriageAdultEbolavirusFemaleHumansMaleMiddle AgedRetrospective StudiesAlgorithmclinical signscontactDemocratic Republic of the CongoEbolaepidemicsexposureoutbreakspredictorssymptomatologysymptomstriage

Identifiers

PMID39447210
PMCPMC11521189

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.