Evidence map›Paper›PMID 38537090›Full record

ArticleRevista de la Facultad de Ciencias Medicas (Cordoba, Argentina)2024

[Predictive appendicitis scale for children under 4 years of age: Is it possible to apply artificial intelligence?]

Dayhana Arango Cárdenas, Jorge Andrés Castrillón Lozano, Ximena Areiza Ocampo

Abstract readEnglish Abstract
In one paragraph

Article in Revista de la Facultad de Ciencias Medicas (Cordoba, Argentina), 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

3 authors.

Dayhana Arango CárdenasEstudiante . dayhana.arangocar@campusucc.edu.co.
Jorge Andrés Castrillón LozanoEstudiante . jorge.castrillon@campusucc.edu.co.
Ximena Areiza OcampoEstudiante . Ximena.areizao@campusucc.edu.co.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute appendicitis in the pediatric population is a pathology of heterogeneous presentation that is currently diagnosed using various criteria or predictive scales, which have proven not to be sufficiently accurate to be standardized, however, methods have been created to establish a more accurate diagnosis, an aspect that has been provided by artificial intelligence, which through different algorithms has the ability to show the patient's condition and the most appropriate intervention for this, thus reducing the rate of unnecessary interventions and therefore possible related complications.

Indexed as

AppendicitisChildChild, PreschoolHumansRetrospective Studiesappendicitisartificial intelligencepediatric

Identifiers

PMID38537090
PMCPMC11110662

What OpenQuestion holds

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LicenceCC BY
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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.