Evidence map›Paper›PMID 36211658›Full record

ArticleFrontiers in public health2022

Developing a guideline for measuring workplace non-Gaussian noise exposure based on kurtosis adjustment of noise level in China.

Meibian Zhang, Yong Hu, Wei Qiu, Xiangjing Gao, Anke Zeng, Zhihao Shi, Jiarui Xin, Shixing Bai, Xin Sun

Abstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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  5. 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.

Meibian ZhangChinese Center for Disease Control and Prevention, National Institute of Occupational Health and Poison Control, Beijing, China.
Yong HuZhejiang Provincial Center for Disease Control and Prevention, Occupational Health and Radiation Protection Institute, Hangzhou, China.
Wei QiuAuditory Research Laboratory, State University of New York at Plattsburgh, Plattsburgh, NY, United States.
Xiangjing GaoZhejiang Provincial Center for Disease Control and Prevention, Occupational Health and Radiation Protection Institute, Hangzhou, China.
Anke ZengChinese Center for Disease Control and Prevention, National Institute of Occupational Health and Poison Control, Beijing, China.
Zhihao ShiJiaxing Center for Disease Control and Prevention, Jiaxing, China.
Jiarui XinChinese Center for Disease Control and Prevention, National Institute of Occupational Health and Poison Control, Beijing, China.
Shixing BaiYangtze Delta Region Institute of Tsinghua University, Jiaxing, China.
Xin SunChinese Center for Disease Control and Prevention, National Institute of Occupational Health and Poison Control, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: There is no unified standard for measuring workplace non-Gaussian noise (known as complex noise) exposure. This study aimed to develop a draft guideline for measuring workplace non-Gaussian complex noise exposure based on noise temporal structure adjustment. Methods: Noise exposure level, e.g., the A-weighted sound pressure level normalized to a nominal 8-h working day (L Results: The framework of the draft guideline included measurement metrics, the protocol using kurtosis to adjust L Conclusion: The draft guideline could provide a basis for accurately measuring workers' exposure to non-Gaussian noise.

Indexed as

Hearing Loss, Noise-InducedNoise, OccupationalChinaHumansWorkplacecomplex noiseguidelinekurtosismeasurementnon-Gaussian noise

Identifiers

PMID36211658
PMCPMC9539429

What OpenQuestion holds

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LicenceCC BY
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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.