Evidence map›Paper›PMID 35909298›Full record

SynthesisJournal of evidence-based medicine2022

Clinical manifestations of COVID-19: An overview of 102 systematic reviews with evidence mapping.

Xufei Luo, Meng Lv, Xianzhuo Zhang, Janne Estill, Bo Yang, Ruobing Lei, Mengjuan Ren, Yunlan Liu, Ling Wang, Xiao Liu and 4 more

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of evidence-based medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  10. Artificial intelligence in triage of COVID-19 patients.Frontiers in artificial intelligence · 2024
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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

14 authors.

Xufei LuoSchool of Public Health, Lanzhou University, Lanzhou, China.
Meng LvChevidence Lab of Child & Adolescent Health, Department of Pediatric Research Institute, Children's Hospital of Chongqing Medical University, Chongqing, China.
Xianzhuo ZhangThe First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
Janne EstillInstitute of Global Health, University of Geneva, Geneva, Switzerland.
Bo YangDepartment of Immune Programming and Management, Shapingba District Center for Disease Control and Prevention of Chongqing, Chongqing, China.
Ruobing LeiChevidence Lab of Child & Adolescent Health, Department of Pediatric Research Institute, Children's Hospital of Chongqing Medical University, Chongqing, China.
Mengjuan RenSchool of Public Health, Lanzhou University, Lanzhou, China.
Yunlan LiuSchool of Public Health, Lanzhou University, Lanzhou, China.
Ling WangSchool of Public Health, Lanzhou University, Lanzhou, China.
Xiao LiuSchool of Public Health, Lanzhou University, Lanzhou, China.
Qi WangFaculty of Health Sciences, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada.
Min MengChevidence Lab of Child & Adolescent Health, Department of Pediatric Research Institute, Children's Hospital of Chongqing Medical University, Chongqing, China.
Yaolong ChenSchool of Public Health, Lanzhou University, Lanzhou, China.ORCID https://orcid.org/0000-0002-7338-4418
COVID-19 evidence and recommendations working group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveCoronavirus disease 2019 (COVID-19) has rapidly spread worldwide, but there is so far no comprehensive analysis of all known symptoms of the disease. Our study aimed to present a comprehensive picture of the clinical symptoms of COVID-19 using an evidence map.

methodsWe systematically searched MEDLINE via PubMed, Web of Science, Embase, and Cochrane library from their inception to March 16, 2021. We included systematic reviews reporting the clinical manifestations of COVID-19 patients. We followed the PRISMA guidelines, and the study selection, data extraction, and quality assessment were done by two individuals independently. We assessed the methodological quality of the studies using AMSTAR. We visually presented the clinical symptoms of COVID-19 and their prevalence.

resultsA total of 102 systematic reviews were included, of which, 68 studies (66.7%) were of high quality, 19 studies (18.6%) of medium quality, and 15 studies (14.7%) of low quality. We identified a total of 74 symptoms including 17 symptoms of the respiratory system, 21 symptoms of the neurological system, 10 symptoms of the gastrointestinal system, 16 cutaneous symptoms, and 10 ocular symptoms. The most common symptoms were fever (67 studies, ranging 16.3%-91.0%, pooled prevalence: 64.6%, 95%CI, 61.3%-67.9%), cough (68 studies, ranging 30.0%-72.2%, pooled prevalence: 53.6%, 95%CI, 52.1%-55.1%), muscle soreness (56 studies, ranging 3.0%-44.0%, pooled prevalence: 18.7%, 95%CI, 16.3%-21.3%), and fatigue (52 studies, ranging 3.3%-58.5%, pooled prevalence: 29.4%, 95%CI, 27.5%-31.3%). The prevalence estimates for COVID-19 symptoms were generally lower in neonates, children and adolescents, and pregnant women than in the general populations.

conclusionAt least 74 different clinical manifestations are associated with COVID-19. Fever, cough, muscle soreness, and fatigue are the most common, but attention should also be paid to the rare symptoms that can help in the early diagnosis of the disease.

Indexed as

COVID-19AdolescentChildCoughFatigueFemaleHumansInfant, NewbornMyalgiaPregnancySARS-CoV-2clinical manifestationsCOVID-19evidence mapSARS-CoV-2systematic review

Identifiers

PMID35909298
PMCPMC9353366

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.