Evidence map›Paper›PMID 33210489›Full record

ArticleZhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences2020

[Standardized sputum collection increases sputum sample collection rate for novel coronavirus nucleic acid detection].

Jianfen Qin, Hongying Pan, Rongrong Zhang, Leiwen Tang, Xiawen Mao

Abstract read
In one paragraph

Article in Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences, 2020. 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
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0citing papers in PubMed
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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

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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

5 authors.

Jianfen QinNursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
Hongying PanNursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
Rongrong ZhangNursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
Leiwen TangNursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
Xiawen MaoNursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the effect of standardized health education on the sputum specimen collection rate for nucleic acid detection of coronavirus disease 2019 (COVID-19).

methodsTwo hundred and twenty-seven patients in fever clinics and isolation wards of Sir Run Run Shaw Hospital of Zhejiang University and 307 migrant workers returning to 5 enterprises in Shanghai from February 3 to March 14, 2020 were enrolled in the study. Through clarifying the procedures of collecting sputum specimens, making graphic/video health education materials, standardizing the contents and methods of health education, we conducted education to the subjects. The subject expectorated spontaneously or with medical assistance. For patients, the number of sampling attempts and sputum acquisition times were documented before and after the implementation of the standardized expectoration method; for the returning migrant employees in the enterprises, only the number of collected samples after the implementation of the standardized expectoration method were recorded.

resultsA total of 378 sputum samples were collected from 227 patients. The sputum sampling rates before and after the implementation of health education were 40.9%and 58.4%, respectively (

conclusionsThe education for standardized sputum sample collection method can effectively increase the sputum collection rate.

Indexed as

Coronavirus InfectionsNucleic Acid Amplification TechniquesPandemicsPneumonia, ViralSpecimen HandlingSputumBetacoronavirusChinaCOVID-19EfficiencyHumansSARS-CoV-2Time FactorsCoronavirus disease 2019Health educationNucleic acid detectionSputum collectionStandardization

Identifiers

PMID33210489
PMCPMC8800755

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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.