Evidence map›Paper›PMID 38561475›Full record

ArticleJournal of exposure science & environmental epidemiology2024

Unmasking the sky: high-resolution PM

Kai Zhang, Jeffrey Lin, Yuanfei Li, Yue Sun, Weitian Tong, Fangyu Li, Lung-Chang Chien, Yiping Yang, Wei-Chung Su, Hezhong Tian and 6 more

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of exposure science & environmental epidemiology, 2024. 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
0cells of the map it votes in
0citing 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

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

16 authors.

Kai ZhangDepartment of Environmental Health Sciences, School of Public Health,University at Albany, State University of New York, Rensselaer, NY, USA. kzhang9@albany.edu.ORCID 0000-0003-0393-5955
Jeffrey LinDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Yuanfei LiAsian Demographic Research Institute, Shanghai University, Shanghai, China.
Yue SunDepartment of International Development, Community, and Environment, Clark University, Worcester, MA, USA.
Weitian TongDepartment of Computer Science, Georgia Southern University, Statesboro, GA, USA.
Fangyu LiDepartment of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Lung-Chang ChienDepartment of Epidemiology and Biostatistics, School of Public Health, University of Nevada, Las Vegas, Las Vegas, NV, USA.
Yiping YangDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Wei-Chung SuDepartment of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Hezhong TianState Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, China.
Peng FuDepartment of Plant Biology, University of Illinois, Urbana, IL, USA.
Fengxiang QiaoInnovative Transportation Research Institute, Texas Southern University, Houston, TX, USA.
Xiaobo Xue RomeikoDepartment of Environmental Health Sciences, School of Public Health,University at Albany, State University of New York, Rensselaer, NY, USA.
Shao LinDepartment of Environmental Health Sciences, School of Public Health,University at Albany, State University of New York, Rensselaer, NY, USA.
Sheng LuoDepartment of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA.
Elena CraftHealth Effects Institute, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough PM

objectiveThis study aimed to predict PM

methodsWe compiled a comprehensive dataset in Texas from 2013 to 2017, including ground-level PM

resultsOur predictive models demonstrate excellent in-sample model performance, as indicated by high R IMPACT STATEMENT: We utilized machine learning approaches to predict PM

Indexed as

Air PollutantsEnvironmental MonitoringMachine LearningParticulate MatterAerosolsAir PollutionHumansParticle SizeSatellite ImageryTexasWeatherAerosolsAir PollutantsParticulate MatterAODGradient boostingMachine learningPM2.5Random forest

Identifiers

PMID38561475

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.