Evidence map›Paper›PMID 38055477›Full record

ArticleRevista brasileira de enfermagem2023

Web App for prediction of hospitalisation in Intensive Care Unit by covid-19.

Greici Capellari Fabrizzio, Alacoque Lorenzini Erdmann, Lincoln Moura de Oliveira

Abstract read
In one paragraph

Article in Revista brasileira de enfermagem, 2023. 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

3 authors.

Greici Capellari FabrizzioUniversidade Federal de Santa Catarina. Florianópolis, Santa Catarina, Brazil.ORCID http://orcid.org/0000-0002-3848-5694
Alacoque Lorenzini ErdmannUniversidade Federal de Santa Catarina. Florianópolis, Santa Catarina, Brazil.ORCID http://orcid.org/0000-0003-4845-8515
Lincoln Moura de OliveiraUniversidade Federal do Ceará. Fortaleza, Ceará, Brazil.ORCID http://orcid.org/0000-0001-6016-745X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop a Web App from a predictive model to estimate the risk of Intensive Care Unit (ICU) admission for patients with covid-19.

methodsAn applied technological production research was carried out with the development of Streamlit using Python, considering the decision tree model that presented the best performance (AUC 0.668).

resultsBased on the variables associated with Precision Nursing, Streamlit stratifies patients admitted to clinical units who are most likely to be admitted to the Intensive Care Unit, serving as a decision-making support tool for healthcare professionals. FINAL CONSIDERATIONS: The performance of the model may have been influenced by the start of vaccination during the data collection period, however, the Web App via Streamlit proved to be a feasible tool for presenting research results, due to the ease of understanding by nurses and its potential for supporting clinical decision-making.

Indexed as

COVID-19Mobile ApplicationsClinical Decision-MakingHospitalizationHumansIntensive Care UnitsRetrospective Studies

Identifiers

PMID38055477
PMCPMC10695068

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.