Evidence map›Paper›PMID 34150150›Full record

ArticleAmerican journal of translational research2021

Retrospective analysis on the clinical characteristics of patients who were reinfected with the Corona Virus in 2019.

Yan Dong, Hanlin Huang, Jun Yang, Liyuan Yang

Abstract read
In one paragraph

Article in American journal of translational research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. 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

4 authors.

Yan DongDepartment of Respiratory Medicine, Xiangyang No. 1 People's Hospital, Hubei University of Medicine Xiangyang, Hubei Province, China.
Hanlin HuangDepartment of Respiratory Medicine, Xiangyang No. 1 People's Hospital, Hubei University of Medicine Xiangyang, Hubei Province, China.
Jun YangDepartment of Respiratory Medicine, Xiangyang No. 1 People's Hospital, Hubei University of Medicine Xiangyang, Hubei Province, China.
Liyuan YangDepartment of Respiratory Medicine, Xiangyang No. 1 People's Hospital, Hubei University of Medicine Xiangyang, Hubei Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to explore the clinical characteristics of 742 patients with re-current Corona Virus Disease in 2019 (COVID-19), so as to provide relevant evidence for clinical diagnosis and treatment of re-infected patients.

methodsAltogether 742 discharged COVID-19 patients were analyzed retrospectively and were divided into re-infected patients (n=60) and non-re-infected patients (n=682) according to whether they became nucleic acid positive again after discharge. The time form leaving the hospital to re-infection and the time form the first nucleic acid negative test results to being re-infected were recorded. The clinical characteristics of the two groups were compared when they were admitted to the hospital. Logistic regression analysis was carried out on disease indicators with statistical differences between the two groups.

resultsCompared with non-re-infected patients, there were statistical differences in age, contact history, fatigue, chills, nasal congestion and runny nose, lung CT observations, clinical classification and lymphocyte count of re-infected patients (P<0.05). Logistic regression analysis showed that nasal congestion and a runny nose, a lymphocyte count less than 0.93×10

conclusionThe symptoms of nasal congestion and runny nose, lymphocyte count less than 0.93×10

Indexed as

Corona Virus Disease 2019recurrent positiverisk factors

Identifiers

PMID34150150
PMCPMC8205718

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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.