Evidence map›Paper›PMID 40864116›Full record

ArticleTropical medicine and infectious disease2025

COVID-19 Clinical Predictors in Patients Treated via a Telemedicine Platform in 2022.

Liliane de Fátima Antonio Oliveira, Lúcia Regina do Nascimento Brahim Paes, Luiz Claudio Ferreira, Gabriel Garcez de Araújo Souza, Guilherme Souza Weigert, Layla Lorena Bezerra de Almeida, Rafael Kenji Fonseca Hamada, Lyz Tavares de Sousa, Andreza Pain Marcelino, Cláudia Maria Valete

Abstract read
In one paragraph

Article in Tropical medicine and infectious disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Liliane de Fátima Antonio OliveiraOswaldo Cruz Foundation (Fiocruz), Evandro Chagas National Institute of Infectious Diseases (INI), Clinical Epidemiology Laboratory, Rio de Janeiro 21040-900, Brazil.ORCID 0000-0002-2714-7139
Lúcia Regina do Nascimento Brahim PaesOswaldo Cruz Foundation (Fiocruz), Evandro Chagas National Institute of Infectious Diseases (INI), Leishmaniasis Clinical Research and Surveillance Laboratory, Rio de Janeiro 21040-360, Brazil.
Luiz Claudio FerreiraOswaldo Cruz Foundation (Fiocruz), Evandro Chagas National Institute of Infectious Diseases (INI), Leishmaniasis Clinical Research and Surveillance Laboratory, Rio de Janeiro 21040-360, Brazil.
Gabriel Garcez de Araújo SouzaConexa Health, Rio de Janeiro 22040-002, Brazil.ORCID 0000-0002-1732-1092
Guilherme Souza WeigertConexa Health, Rio de Janeiro 22040-002, Brazil.
Layla Lorena Bezerra de AlmeidaConexa Health, Rio de Janeiro 22040-002, Brazil.
Rafael Kenji Fonseca HamadaConexa Health, Rio de Janeiro 22040-002, Brazil.
Lyz Tavares de SousaConexa Health, Rio de Janeiro 22040-002, Brazil.
Andreza Pain MarcelinoOswaldo Cruz Foundation (Fiocruz), Evandro Chagas National Institute of Infectious Diseases (INI), Leishmaniasis Clinical Research and Surveillance Laboratory, Rio de Janeiro 21040-360, Brazil.ORCID 0000-0002-7197-2128
Cláudia Maria ValeteOswaldo Cruz Foundation (Fiocruz), Evandro Chagas National Institute of Infectious Diseases (INI), Leishmaniasis Clinical Research and Surveillance Laboratory, Rio de Janeiro 21040-360, Brazil.

Funding

Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ) 75635131Coordination for the Improvement of Higher Education Personnel - Brazil (CAPES) 001
6 · The paper itself

Abstract

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus, whose 2020 outbreak was characterized as a pandemic by the World Health Organization. Restriction measures changed healthcare delivery, with telehealth providing a viable alternative throughout the pandemic. This study analyzed a telemedicine platform database with the goal of developing a diagnostic prediction model for COVID-19 patients. This is a longitudinal study of patients seen on the Conexa Saúde telemedicine platform in 2022. A multiple binary logistic regression model of controls (negative confirmation for COVID-19 or confirmation of other influenza-like illness) versus COVID-19 was developed to obtain an odds ratio (OR) and a 95% confidence interval (CI). In the final binary logistic regression model, six factors were considered significant: presence of rhinorrhea, ocular symptoms, abdominal pain, rhinosinusopathy, and wheezing/asthma and bronchospasm were more frequent in controls, thus indicating a greater chance of flu-like illnesses than COVID-19. The presence of tiredness and fatigue was three times more prevalent in COVID-19 cases (OR = 3.631; CI = 1.138-11.581;

Indexed as

COVID-19SARSCoV-2 virustelemedicine

Identifiers

PMID40864116
PMCPMC12390043

What OpenQuestion holds

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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.