Evidence map›Paper›PMID 41029526›Full record

ArticleBMC cancer2025

Long-term exposures to PM

Jiangjie Zhou, Baosheng Liang, Mintao Su, Zhenyu Zhang, Jing Li, Yunfeng Xi, Dawei Zhu, Yao Yao, Shengfeng Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Jiangjie ZhouDepartment of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Baosheng LiangDepartment of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China. liangbs@hsc.pku.edu.cn.
Mintao SuDepartment of Global Health, School of Public Health, Peking University, Beijing, 100191, China.
Zhenyu ZhangDepartment of Global Health, School of Public Health, Peking University, Beijing, 100191, China.
Jing LiInstitute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China.
Yunfeng XiCenter for Disease Control and Prevention, Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, 010031, China. xiyunfeng210@163.com.
Dawei ZhuChina Health Development Research Center, Peking University, Beijing, 100191, China.
Yao YaoChina Health Development Research Center, Peking University, Beijing, 100191, China.
Shengfeng WangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China. wangshengfeng@bjmu.edu.cn.

Funding

Beijing Municipal Health Commission 2024-1G-4251
6 · The paper itself

Abstract

backgroundThis study aims to investigate the association between long-term exposure to PM

methodsThis study adopted a retrospective cohort design, included 17,230 female breast cancer patients in Inner Mongolia from 2012 to 2021, collected daily average concentration of air pollutants in Inner Mongolia, and death causes. Non-parametric hypothesis tests were used to test the significance of breast-cancer cure rate. Semiparametric mixture cure model was utilized to analyze the effects of PM

resultsNon-parametric test on the 8-year cure rate of 93.6% is significant (P < 0.001). The odds ratios (ORs) for unite increment per interquartile range (IQR) in PM

conclusionLong-term exposure to high-concentration PM

Indexed as

Air PollutantsAir PollutionBreast NeoplasmsEnvironmental ExposureParticulate MatterAdultAgedChinaFemaleHumansMiddle AgedRetrospective StudiesAir PollutantsParticulate MatterAir pollutionBreast cancerBreast cancer-specific mortalityMixture cure modelPM2.5 components

Identifiers

PMID41029526
PMCPMC12482088

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.