Evidence map›Paper›PMID 36477624›Full record

ArticleScientific reports2022

Diagnostic value of "hyperdense consolidation sign" as a characteristic new computed tomography sign of diffuse alveolar hemorrhage.

Soma Kumasaka, Yuka Kumasaka, Akiko Jingu, Yoshito Tsushima

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. 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
0.6field-weighted citation impact, top 30% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

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

4 authors at 1 institution in 1 country.

Soma KumasakaDepartment of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, 3-39-22 Showa-Machi, Maebashi, Gunma, 371-8511, Japan. kumasaka88@gunma-u.ac.jp.ORCID 0000-0001-5541-3847
Yuka KumasakaDepartment of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, 3-39-22 Showa-Machi, Maebashi, Gunma, 371-8511, Japan.
Akiko JinguDepartment of Diagnostic Radiology, Fujioka General Hospital, 813-1 Nakakurisu, Fujioka, Gunma, Japan.
Yoshito TsushimaDepartment of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, 3-39-22 Showa-Machi, Maebashi, Gunma, 371-8511, Japan.
Gunma University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diffuse alveolar hemorrhage (DAH) is an uncommon but life-threatening condition. Although DAH must be distinguished from other lung diseases, no specific computed tomography (CT) signs of DAH have been reported. This study aimed to evaluate the diagnostic value of "hyperdense consolidation" CT sign. We retrospectively evaluated non-contrast CT findings of 25 DAH patients and age- (≤ 2 years) and sex-matched controls with symptoms of dyspnea and hypoxemia. Two radiologists compared the two groups for the presence of hyperdense consolidation signs in lung parenchyma, defined as consolidation that visually contains areas with higher density than the aorta in the specific narrow window setting (window level = 35 Hounsfield units [HU], width = 80 HU) with a mediastinal filter. The sensitivity, specificity, positive- and negative-predictive values of the hyperdense consolidation sign for detection of DAH were 32.0%, 100%, 100%, and 59.5% with perfect interobserver agreement (к = 1.00). The hyperdense consolidation sign was found to be a highly specific sign for DAH.

Indexed as

Lung DiseasesChild, PreschoolHemorrhageHumansRetrospective StudiesTomographyTomography, X-Ray Computed

Identifiers

PMID36477624
PMCPMC9729609
OpenAlexW4311580202

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Registered trials

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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.