Evidence map›Paper›PMID 34475770›Full record

ArticleClinical, cosmetic and investigational dermatology2021

Teledermatology Service During the COVID-19 Pandemic in China: A Mobile Application-Based Retrospective Study.

Zhanglei Mu, Xiaojing Liu, Kun Li, Jianzhong Zhang

Open access · goldAbstract read
In one paragraph

Article in Clinical, cosmetic and investigational dermatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 29% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

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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

4 authors at 3 institutions in 1 country.

Zhanglei MuDepartment of Dermatology, Peking University People's Hospital, Beijing, People's Republic of China.
Xiaojing LiuDepartment of Dermatology, Shanghai Skin Disease Hospital, Tongji University, Shanghai, People's Republic of China.
Kun LiDepartment of Dermatology, Peking University People's Hospital, Beijing, People's Republic of China.
Jianzhong ZhangDepartment of Dermatology, Peking University People's Hospital, Beijing, People's Republic of China.
Peking University People's Hospital · CNPeking University · CNShanghai Skin Disease Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDuring the COVID-19 pandemic, teledermatology service was increased rapidly. The purpose of our study was to analyze the characteristics of patients and common skin diseases via teledermatology during the COVID-19 pandemic in mainland China. PATIENTS AND

methodsDuring weekends between January 21 and April 4, 2020, the data of patients who used teledermatology service via a mobile application were collected, including gender, age, and diagnosis.

resultsA total of 698 patients (315 men and 383 women), with a mean age of 26 years, used this service. The top ten common diseases in order of proportion were eczema (22%), acne (9%), atopic dermatitis (9%), urticaria (5%), contact dermatitis (5%), herpes zoster (3%), warts (3%), folliculitis (3%), prurigo (3%), and androgenetic alopecia (2%). When classified according to age groups, atopic dermatitis was the most common condition for patients in the first decade, acne was more prevalent in the second and third decades, and eczema was the most prevalent condition for all other age groups.

conclusionThe ten common diseases accounted for the majority of the evaluated cases and varied by age group, allowing individualizing teledermatology services.

Indexed as

case mixChinaclinical epidemiologyCOVID-19dermatologyteledermatology

Identifiers

PMID34475770
PMCPMC8407777
OpenAlexW3197464891

What OpenQuestion holds

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LicenceCC BY-NC
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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.