Evidence map›Paper›PMID 41287854›Full record

ArticlePeerJ2025

Unveiling the clinical profiles of critically ill COVID-19 patients: insights from Ezhou during the early spread.

Guohui Yang, Zewen Liu, Tabitha Abraham, Linwei Li, Tingyang Zhou, Qing Zhang, Li Zuo

Abstract read
In one paragraph

Article in PeerJ, 2025. 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
–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

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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

7 authors.

Guohui Yang *Department of Intensive Medicine, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Zewen Liu *Department of Anesthesiology, Affiliated Ezhou Central Hospital of Wuhan University, Ezhou, China.
Tabitha AbrahamThe University of Texas Rio Grande Valley School of Medicine, Edinburg, TX, United States of America.
Linwei LiThe University of Texas Rio Grande Valley School of Medicine, Edinburg, TX, United States of America.
Tingyang ZhouThe Interdisciplinary Biophysics Graduate Program, The Ohio State University, Columbus, OH, United States of America.
Qing ZhangDepartment of Intensive Medicine, Affiliated Ezhou Central Hospital of Wuhan University, Ezhou, China.
Li ZuoDepartment of Physical Therapy, Medical Education, and Neuroscience, The University of Texas and UT Health Rio Grande Valley, School of Medicine, College of Health Professions, Edinburg, TX, United States of America.

Funding

Natural Science Foundation of Hubei Province
6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) started in December 2019 in Wuhan. This article evaluated clinical characteristics, and imaging manifestations in Ezhou, an early locked-down city, 80 kilometers east of Wuhan. We analyzed data from 98 confirmed severe COVID-19 patients in Ezhou Central Hospital between February 1st and March 22nd, 2020. We compared patients' laboratory results, imaging manifestations, and treatments between survival and death groups. Of these 98 confirmed COVID-19 patients, 24 individuals (24.6%) had chronic diseases. The main symptoms of critically ill patients were fever (86.7%), cough (83.7%), and dyspnea (59.1%). Common complications were acute respiratory distress syndrome (ARDS; 49%), acute kidney injury (37.7%), and multiple organ dysfunction syndrome (MODS; 32.6%). Computed tomography (CT) scans displayed ground-glass opacity at subpleural regions that were associated with interlobular septal thickening. Within 28 days, 39 (39.8%) patients died. Compared to survivors, the death group had a higher median age (69.8

Indexed as

COVID-19Critical IllnessAdultAgedAged, 80 and overChinaFemaleHumansMaleMiddle AgedMultiple Organ FailureRespiratory Distress SyndromeSARS-CoV-2Tomography, X-Ray ComputedClinical characteristicsHigh-resolution CT (HRCT)MortalitySARS-CoV-2

Identifiers

PMID41287854
PMCPMC12640636

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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.