Evidence map›Paper›PMID 36309548›Full record

ArticleScientific reports2022

Infection control for COVID-19 in hospital examination room.

Mamoru Takada, Taichi Fukushima, Sho Ozawa, Syuma Matsubara, Takeshi Suzuki, Ichiro Fukumoto, Toyoyuki Hanazawa, Takeshi Nagashima, Reiko Uruma, Masayuki Otsuka and 1 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 24% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
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

11 authors at 1 institution in 1 country.

Mamoru TakadaSafety and Health Organization, Chiba University, 1-33, Yayoi-Cho, Inage-ku, Chiba, Chiba, Japan. mamoru@chiba-u.jp.
Taichi FukushimaDepartment of Mechanical Engineering, Graduate School of Engineering, Chiba University, Chiba, Japan.
Sho OzawaDepartment of Mechanical Engineering, Graduate School of Engineering, Chiba University, Chiba, Japan.
Syuma MatsubaraDepartment of Mechanical Engineering, Graduate School of Engineering, Chiba University, Chiba, Japan.
Takeshi SuzukiDepartment of Otorhinolaryngology/Head and Neck Surgery, Chiba University, Graduate School of Medicine, Chiba, Japan.
Ichiro FukumotoDepartment of Otorhinolaryngology/Head and Neck Surgery, Chiba University, Graduate School of Medicine, Chiba, Japan.
Toyoyuki HanazawaDepartment of Otorhinolaryngology/Head and Neck Surgery, Chiba University, Graduate School of Medicine, Chiba, Japan.
Takeshi NagashimaDepartment of General Surgery, Chiba University, Graduate School of Medicine, Chiba, Japan.
Reiko UrumaSafety and Health Organization, Chiba University, 1-33, Yayoi-Cho, Inage-ku, Chiba, Chiba, Japan.
Masayuki OtsukaDepartment of General Surgery, Chiba University, Graduate School of Medicine, Chiba, Japan.
Gaku TanakaDepartment of Mechanical Engineering, Graduate School of Engineering, Chiba University, Chiba, Japan.
Chiba University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Healthcare providers are vulnerable to infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) because of their close proximity to patients with coronavirus disease 2019. SARS-CoV-2 is mainly transmitted via direct and indirect contact with respiratory droplets, and its airborne transmission has also been identified. However, evidence for environmental factors is scarce, and evidence-based measures to minimize the risk of infection in clinical settings are insufficient. Using computational fluid dynamics, we simulated exhalation of large and small aerosol particles by patients in an otolaryngology examination room, where medical procedures require the removal of a face mask. The effects of coughing were analyzed, as well as those of humidity as a controllable environmental factor and of a suction device as an effective control method. Our results show that a suction device can minimize aerosol exposure of healthcare workers by efficiently removing both large (11.6-98.2%) and small (39.3-99.9%) aerosol particles. However, for coughing patients, the removal efficiency varies inversely with the particle size, and the humidity notably affects the aerosol behavior, indicating the need for countermeasures against smaller aerosols. Overall, these results highlight the potential and limitation of using a suction device to protect against SARS-CoV-2 and future respiratory infections.

Indexed as

COVID-19CoughHospitalsHumansInfection ControlInfectious Disease Transmission, Patient-to-ProfessionalRespiratory Aerosols and DropletsSARS-CoV-2

Identifiers

PMID36309548
PMCPMC9617229
OpenAlexW4307812777

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.