Evidence map›Paper›PMID 33126309›Full record

ArticleMedicine2020

Management of a "suspected ward" in a COVID-19 designated hospital in Wuhan.

Meng Kuang, Lanrong Zheng, Chun Li, Liuqing Sheng, Min Qi, Huiping Deng, Chiqiu Jiang

Abstract read
In one paragraph

Article in Medicine, 2020. 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

7 authors.

Meng KuangNeonatal Department, Jianghan University Affiliated Hubei Third People's Hospital, Wuhan.
Lanrong ZhengDepartment of Pathoanatomy, Wannan Medical College, Wuhu.ORCID 0000-0001-7482-1491
Chun LiLaboratory of Ophthalmology and immunology, Zhongshan Ophthalmology Center, Sun Yat Sen University, Guangzhou.
Liuqing ShengNeurosurgery, Jianghan University Affiliated Hubei Third People's Hospital, Wuhan, China.
Min QiDepartment of Pathoanatomy, Wannan Medical College, Wuhu.
Huiping DengNeonatal Department, Jianghan University Affiliated Hubei Third People's Hospital, Wuhan.
Chiqiu JiangNeonatal Department, Jianghan University Affiliated Hubei Third People's Hospital, Wuhan.ORCID 0000-0002-8007-5429

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During December 2019, an outbreak of unexplained pneumonia occurred in Wuhan, Hubei Province. The disease was subsequently named coronavirus disease 2019 (COVID-19) and the causative virus as severe acute respiratory syndrome conronavirus-2 (SARS-CoV-2). Based on experience, it is vital to exclude or diagnose suspected patients as soon as possible to prevent disease spread. Our hospital is a COVID-19 designated hospital in Wuhan. During the epidemic period, there was a reconstruction of the medical facilities to accommodate patients with different disease status. We document the development of "suspected ward," a ward that cared for patients with suspected COVID-19, in a large designated hospital during the COVID-19 outbreak in Wuhan City, China, and explain the suspected ward spatial layout, organization structure, diagnosis, and treatment flow chart of suspected cases. The key characteristics of our "suspected ward" is isolation, triage, fast diagnosis, and rapid referral. Our description of this suspected ward provides a reference for further improvements in the care of patients with suspected disease in emergency medical institutions.

Indexed as

BetacoronavirusChinaCoronavirus InfectionsCOVID-19Hospital DepartmentsHumansInfectious Disease MedicinePandemicsPneumonia, ViralQuality ImprovementSARS-CoV-2

Identifiers

PMID33126309
PMCPMC7598855

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.