Evidence map›Paper›PMID 35608611›Full record

ArticleEmerging infectious diseases2022

Risk Prediction Score for Pediatric Patients with Suspected Ebola Virus Disease.

Alicia E Genisca, Tzu-Chun Chu, Lawrence Huang, Monique Gainey, Moyinoluwa Adeniji, Eta N Mbong, Stephen B Kennedy, Razia Laghari, Fiston Nganga, Rigo F Muhayangabo and 5 more

Open access · diamondAbstract read
In one paragraph

Article in Emerging infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 51% of its field
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

3 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
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

15 authors at 4 institutions in 1 country.

Alicia E Genisca
Tzu-Chun Chu
Lawrence Huang
Monique Gainey
Moyinoluwa Adeniji
Eta N Mbong
Stephen B Kennedy
Razia Laghari
Fiston Nganga
Rigo F Muhayangabo
Himanshu Vaishnav
Shiromi M Perera
Andrés Colubri
Adam C Levine
Ian C Michelow
International Medical Corps · USBrown University · USRhode Island Hospital · USUniversity of Massachusetts Chan Medical School · US

Funding

Emerging Infectious Disease Scholars at Brown UniversityR25AI140490 · NIAID · BROWN UNIVERSITY · PI Silvia Shinpei Chiang · 2018 to 2026
$3.0M
NIAID NIH HHS R25 AI140490
6 · The paper itself

Abstract

Rapid diagnostic tools for children with Ebola virus disease (EVD) are needed to expedite isolation and treatment. To evaluate a predictive diagnostic tool, we examined retrospective data (2014-2015) from the International Medical Corps Ebola Treatment Centers in West Africa. We incorporated statistically derived candidate predictors into a 7-point Pediatric Ebola Risk Score. Evidence of bleeding or having known or no known Ebola contacts was positively associated with an EVD diagnosis, whereas abdominal pain was negatively associated. Model discrimination using area under the curve (AUC) was 0.87, which outperforms the World Health Organization criteria (AUC 0.56). External validation, performed by using data from International Medical Corps Ebola Treatment Centers in the Democratic Republic of the Congo during 2018-2019, showed an AUC of 0.70. External validation showed that discrimination achieved by using World Health Organization criteria was similar; however, the Pediatric Ebola Risk Score is simpler to use.

Indexed as

EbolavirusHemorrhagic Fever, EbolaArea Under CurveChildDemocratic Republic of the CongoDisease OutbreaksHumansRetrospective StudiesRisk FactorschildrenEbola virus diseaserisk prediction scorevirusesWest Africa

Identifiers

PMID35608611
PMCPMC9155869
OpenAlexW4280615432

What OpenQuestion holds

Textmetadata
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.