Evidence map›Paper›PMID 35456296›Full record

ArticleJournal of clinical medicine2022

Combined Radiographic Features and Age Can Distinguish

Yuto Iwanaga, Kei Yamasaki, Kazuki Nemoto, Kentaro Akata, Hiroaki Ikegami, Keigo Uchimura, Shingo Noguchi, Chinatsu Nishida, Toshinori Kawanami, Kazumasa Fukuda and 2 more

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

12 authors.

Yuto IwanagaDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan.
Kei YamasakiDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan.ORCID 0000-0003-1876-3287
Kazuki NemotoDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan.
Kentaro AkataDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan.
Hiroaki IkegamiDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan.
Keigo UchimuraDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan.ORCID 0000-0001-6379-9583
Shingo NoguchiDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan.
Chinatsu NishidaDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan.
Toshinori KawanamiDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan.
Kazumasa FukudaDepartment of Microbiology, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan.
Hiroshi MukaeDepartment of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8523, Japan.
Kazuhiro YateraDepartment of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan.ORCID 0000-0002-2955-4432

Funding

Research Grant from the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientific Research (C) 23591173
6 · The paper itself

Abstract

The study objective was to evaluate chest radiographic features that distinguish Mycoplasma pneumoniae pneumonia (MPP) from other bacterial pneumonias diagnosed based on the bacterial floral analysis with 16S rRNA gene sequencing, using bronchoalveolar lavage fluid samples directly obtained from pneumonia lesions. Patients were grouped according to the dominant bacterial phenotype; among 120 enrolled patients with CAP, chest CT findings were evaluated in 55 patients diagnosed with a mono-bacterial infection (one bacterial phylotype occupies more than 80% of all phylotypes in a sample) by three authorized respiratory physicians. Among this relatively small sample size of 55 patients with CAP, 10 had MPP, and 45 had other bacterial pneumonia and were categorized into four groups according to their predominant bacterial phylotypes. We created a new scoring system to discriminate MPP from other pneumonias, using a combination of significant CT findings that were observed in the M. pneumoniae group, and age (<60 years) (MPP−CTA scoring system). When the cutoff value was set to 1, this scoring system had a sensitivity of 80%, a specificity of 93%, a positive predictive value of 73%, and a negative predictive value of 95%. Among the CT findings, centrilobular nodules were characteristic findings in patients with MPP, and a combination of chest CT findings and age might distinguish MPP from other bacterial pneumonias.

Indexed as

16S rRNA genebronchoalveolar lavage fluidcentrilobular noduleschest computed tomographyclone library analysisMycoplasma pneumoniae pneumonia

Identifiers

PMID35456296
PMCPMC9032927

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.