Evidence map›Paper›PMID 39184708›Full record

ArticleCureus2024

Stratification of Group A Streptococcal Pharyngitis Children Using Unsupervised Learning.

Yoshifumi Miyagi

Abstract read
In one paragraph

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

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

1 author.

Yoshifumi MiyagiDepartment of Pediatrics, Haibara General Hospital, Shizuoka, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and objectives Group A Streptococcus (GAS) is the most frequent cause of bacterial pharyngitis, and it is advised to selectively use rapid antigen detection testing (RADT). Currently, the decision to perform this test is based on pediatricians' observations, but the criteria are not well-defined. Therefore, we utilized unsupervised learning to categorize patients based on the clinical manifestations of GAS pharyngitis. Our goal was to pinpoint the clinical symptoms that should prompt further examination and treatment in patients diagnosed with pharyngitis. Methods We analyzed categorical data from 305 RADT-positive patients aged three to 15 years using the K-modes clustering method. Each explanatory variable's relationship with cluster variables was statistically examined. Finally, we tested the differences between clusters for continuous variables statistically. Results The K-modes method categorized the cases into two clusters. Cluster 1 included older children with lymph node tenderness, while Cluster 2 consisted of younger children with cough and rhinorrhea. Conclusion Differentiating streptococcal pharyngitis from common cold or upper respiratory tract infection based on clinical symptoms alone is challenging, particularly in young patients. Future research should focus on identifying indicators that can aid in suspecting streptococcal infection in young patients.

Indexed as

group a streptococcusk-modespcapharyngitisunsupervised learning

Identifiers

PMID39184708
PMCPMC11345101

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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.