Evidence map›Paper›PMID 39511240›Full record

ArticleScientific reports2024

Redivision of reference intervals of the red blood cell distribution width in a healthy Chinese population based on natural geographic environment.

Lei Zhang, Shuyan Yin, Miao Ge, Congxia Wang, Chuwan He

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Lei ZhangSchool of Geography and Tourism, Shaanxi Normal University, 620 West Chang'an Street, Chang'an District, Xi'an, 710119, China.
Shuyan YinSchool of Geography and Tourism, Shaanxi Normal University, 620 West Chang'an Street, Chang'an District, Xi'an, 710119, China. yinshy@snnu.edu.cn.
Miao GeSchool of Geography and Tourism, Shaanxi Normal University, 620 West Chang'an Street, Chang'an District, Xi'an, 710119, China.
Congxia WangDepartment of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University, 157 West 5th Road, Xincheng District, Xi'an, 710004, China.
Chuwan HeDepartment of Haematology and Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, 69 Xijuyuan Lane, Lianhu District, Xi'an, 710000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Revealing the spatial distribution and regional differences of red blood cell distribution width (RDW) reference values in the healthy Chinese population is important for the monitoring, diagnosis and prevention of the top chronic diseases in the disease spectrum in China. Medical indicator data were matched with geographical data and environmental pollution data to predict RDW reference values for the whole region of China using ridge regression analysis, support vector machine and BP neural network. Taylor diagrams and assignment methods were used to select the optimal model, and the spatial distribution was mapped according to the prediction results of the optimal model, and the intervals of RDW reference values for different age groups of healthy people in China were redefined. It was suggested that the reference values of RDW in the young and middle-aged groups in China were divided into low-value areas in the north (12.31,12.80) and high-value areas in the south (12.80,13.18); the reference values of RDW in the elderly group were divided into low-value areas in the north (11.28,12.60) and high-value areas in the south (12.60,13.91).In turn, it can provide a more accurate reference basis in the monitoring, diagnosis and prevention of chronic diseases at the top of the disease spectrum.

Indexed as

Erythrocyte IndicesReference ValuesAdolescentAdultAgedChildChinaEast Asian PeopleErythrocytesFemaleGeographyHumansMaleMiddle AgedNeural Networks, ComputerSupport Vector MachineDisease spectrumGeographical environment factorsInterval divisionRed blood cell distribution widthSpatial distribution map

Identifiers

PMID39511240
PMCPMC11544022

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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.