Evidence map›Paper›PMID 33304856›Full record

ArticleFrontiers in cellular and infection microbiology2020

Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number.

Eun-Hye Kim, Seunghoon Kim, Hyun-Joo Kim, Hyoung-Oh Jeong, Jaewoong Lee, Jinho Jang, Ji-Young Joo, Yerang Shin, Jihoon Kang, Ae Kyung Park and 2 more

Abstract read
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In one paragraph

Article in Frontiers in cellular and infection microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 5 pooled it
–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

36 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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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

12 authors.

Eun-Hye KimDepartment of R&D, Helixco Inc., Ulsan, South Korea.
Seunghoon KimDepartment of Biomedical Engineering, College of Information-Bio Convergence Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.
Hyun-Joo KimDepartment of Periodontology, Dental and Life Science Institute, Pusan National University, School of Dentistry, Yangsan, South Korea.
Hyoung-Oh JeongDepartment of Biomedical Engineering, College of Information-Bio Convergence Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.
Jaewoong LeeDepartment of Biomedical Engineering, College of Information-Bio Convergence Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.
Jinho JangDepartment of Biomedical Engineering, College of Information-Bio Convergence Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.
Ji-Young JooDepartment of Periodontology, Dental and Life Science Institute, Pusan National University, School of Dentistry, Yangsan, South Korea.
Yerang ShinDepartment of R&D, Helixco Inc., Ulsan, South Korea.
Jihoon KangDepartment of R&D, Helixco Inc., Ulsan, South Korea.
Ae Kyung ParkCollege of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon, South Korea.
Ju-Youn LeeDepartment of Periodontology, Dental and Life Science Institute, Pusan National University, School of Dentistry, Yangsan, South Korea.
Semin LeeDepartment of Biomedical Engineering, College of Information-Bio Convergence Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Periodontitis is a widespread chronic inflammatory disease caused by interactions between periodontal bacteria and homeostasis in the host. We aimed to investigate the performance and reliability of machine learning models in predicting the severity of chronic periodontitis. Mouthwash samples from 692 subjects (144 healthy controls and 548 generalized chronic periodontitis patients) were collected, the genomic DNA was isolated, and the copy numbers of nine pathogens were measured using multiplex qPCR. The nine pathogens are as follows:

Indexed as

Chronic PeriodontitisAggregatibacter actinomycetemcomitansDNA Copy Number VariationsHumansMachine LearningPeptostreptococcusPorphyromonas gingivalisReproducibility of Resultschronic periodontitismachine learningmultiplex qPCRsalivary bacterial copy numberseverity predictionslight periodontitis

Identifiers

PMID33304856
PMCPMC7701273

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.