Evidence map›Paper›PMID 36164582›Full record

ArticleBuilding and environment2022

Bioaerosol distribution characteristics and potential SARS-CoV-2 infection risk in a multi-compartment dental clinic.

Zhijian Liu, Guangpeng Yao, Yabin Li, Zhenzhe Huang, Chuan Jiang, Junzhou He, Minnan Wu, Jia Liu, Haiyang Liu

Open access · greenAbstract read
In one paragraph

Article in Building and environment, 2022. 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
14.3field-weighted citation impact, top 1% 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

2 citing papers in PubMed, 44 citations in OpenAlex.

  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

9 authors at 2 institutions in 1 country.

Zhijian LiuDepartment of Power Engineering, North China Electric Power University, Baoding, Hebei, 071003, PR China.
Guangpeng YaoDepartment of Power Engineering, North China Electric Power University, Baoding, Hebei, 071003, PR China.
Yabin LiThe Fifth Medical Center of PLA General Hospital, Beijing, 100039, China.
Zhenzhe HuangDepartment of Power Engineering, North China Electric Power University, Baoding, Hebei, 071003, PR China.
Chuan JiangDepartment of Power Engineering, North China Electric Power University, Baoding, Hebei, 071003, PR China.
Junzhou HeDepartment of Power Engineering, North China Electric Power University, Baoding, Hebei, 071003, PR China.
Minnan WuDepartment of Power Engineering, North China Electric Power University, Baoding, Hebei, 071003, PR China.
Jia LiuThe Fifth Medical Center of PLA General Hospital, Beijing, 100039, China.
Haiyang LiuDepartment of Power Engineering, North China Electric Power University, Baoding, Hebei, 071003, PR China.
North China Electric Power University · CNChinese PLA General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dental clinics have a potential risk of infection, particularly during the COVID-19 pandemic. Multi-compartment dental clinics are widely used in general hospitals and independent clinics. This study utilised computational fluid dynamics to investigate the bioaerosol distribution characteristics in a multi-compartment dental clinic through spatiotemporal distribution, working area time-varying concentrations, and key surface deposition. The infection probability of SARS-CoV-2 for the dental staff and patients was calculated using the Wells-Riley model. In addition, the accuracy of the numerical model was verified by field measurements of aerosol concentrations performed during a clinical ultrasonic scaling procedure. The results showed that bioaerosols were mainly distributed in the compartments where the patients were treated. The average infection probability was 3.8% for dental staff. The average deposition number per unit area of the treatment chair and table are 28729 pcs/m

Indexed as

Airborne transmissionCOVID-19Risk assessmentWells–Riley model

Identifiers

PMID36164582
PMCPMC9494923
OpenAlexW4296691877

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.