Evidence map›Paper›PMID 35979156›Full record

ArticlePolish journal of radiology2022

Diagnostic accuracy and prognostic value of lung ultrasound in coronavirus disease (COVID-19).

Javid Azadbakht, Maryam Saffari, Hamidreza Talarie, Mahsa Masjedi Esfahani, Mahdi Barzegar

Open access · diamondAbstract read
In one paragraph

Article in Polish journal of radiology, 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
0.6field-weighted citation impact, top 34% of its field
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, 4 citations in OpenAlex.

  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

5 authors at 2 institutions in 1 country.

Javid AzadbakhtDepartment of Radiology, Faculty of Medicine, Kashan University of Medical Sciences, Kashan, Iran.
Maryam SaffariDepartment of Radiology, Faculty of Medicine, Kashan University of Medical Sciences, Kashan, Iran.
Hamidreza TalarieDepartment of Radiology, Faculty of Medicine, Kashan University of Medical Sciences, Kashan, Iran.
Mahsa Masjedi EsfahaniDepartment of Radiology, Faculty of Medicine, Kashan University of Medical Sciences, Kashan, Iran.
Mahdi BarzegarDepartment of Neurology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Kashan University of Medical Sciences · IRIsfahan University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aimed to assess the correlation between lung ultrasound (LUS) and computed tomography (CT) findings and the predictability of LUS scores to anticipate disease characteristics, lab data, clinical severity, and mortality in patients with COVID-19. Material and methods: Fifty consecutive hospitalized PCR-confirmed COVID-19 patients who underwent chest CT scan and LUS on the first day of admission were enrolled. The LUS score was calculated based on the presence, severity, and distribution of parenchymal abnormalities in 14 regions. Results: The participants' mean age was 54.60 ± 19.93 years, and 26 (52%) were female. All patients had CT and LUS findings typical of COVID-19. The mean value of CT and LUS severity scores were 11.80 ± 3.89 (ranging from 2 to 20) and 13.74 ± 6.43 (ranging from 1 to 29), respectively. The LUS score was significantly higher in females ( Conclusions: Our results suggested that LUS can be used as a valuable tool for detecting COVID-19 pneumonia and determining high-risk hospitalized patients, helping to triage and stratify high-risk patients, which waives the need to undertake irradiating chest CT and reduces the burden of overworked CT department staff.

Indexed as

computed tomography scancoronavirusCOVID-19lung ultrasound

Identifiers

PMID35979156
PMCPMC9373868
OpenAlexW4293311566

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.