Evidence map›Paper›PMID 35765412›Full record

ArticleGeoHealth2022

Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation.

Luke Conibear, Carly L Reddington, Ben J Silver, Ying Chen, Christoph Knote, Stephen R Arnold, Dominick V Spracklen

Abstract read
In one paragraph

Article in GeoHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

7 authors.

Luke ConibearSchool of Earth and Environment Institute for Climate and Atmospheric Science University of Leeds Leeds UK.ORCID https://orcid.org/0000-0003-2801-8862
Carly L ReddingtonSchool of Earth and Environment Institute for Climate and Atmospheric Science University of Leeds Leeds UK.ORCID https://orcid.org/0000-0002-5990-4966
Ben J SilverSchool of Earth and Environment Institute for Climate and Atmospheric Science University of Leeds Leeds UK.ORCID https://orcid.org/0000-0003-0395-0637
Ying ChenCollege of Engineering Mathematics and Physical Sciences University of Exeter Exeter UK.ORCID https://orcid.org/0000-0002-0319-4950
Christoph KnoteFaculty of Medicine University of Augsburg Augsburg Germany.ORCID https://orcid.org/0000-0001-9105-9179
Stephen R ArnoldSchool of Earth and Environment Institute for Climate and Atmospheric Science University of Leeds Leeds UK.ORCID https://orcid.org/0000-0002-4881-5685
Dominick V SpracklenSchool of Earth and Environment Institute for Climate and Atmospheric Science University of Leeds Leeds UK.ORCID https://orcid.org/0000-0002-7551-4597

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning models can emulate chemical transport models, reducing computational costs and enabling more experimentation. We developed emulators to predict annual-mean fine particulate matter (PM

Indexed as

air qualityChinaemulatorhealth impact assessmentmachine learningparticulate matter

Identifiers

PMID35765412
PMCPMC9207901

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.