Evidence map›Paper›PMID 40399369›Full record

ArticleScientific reports2025

Head injury risk prediction for vulnerable road users based on Chinese adult male head data.

Ying Lu, Jun Bai, Yufa Liu, Yu Shu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. 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

4 authors.

Ying LuSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China. luying@ujs.edu.cn.
Jun BaiSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China.
Yufa LiuSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China.
Yu ShuFourth People's Hospital of Zhenjiang, Zhenjiang, 212013, China.

Funding

Key Research and Development Plan of Zhenjiang City SH2019054National Key Research and Development Program of China 2019YFB1600500National Natural Science Foundation of China 51605197Natural Science Foundation of Jiangsu Province BK20160524
6 · The paper itself

Abstract

Prediction of injuries to vulnerable road users (VRUs) during the head-ground collision phase has been a long-standing challenges in accident modeling. This study aims to reveal the severity of head injury in vehicle-VRU collision (VVC) accidents and quantify the relationship between the head-ground collision (HGC) velocity and the injury levels of brain tissue with local human attributes. First, a finite element head model with Chinese human attributes was constructed and verified. The simulation model of the HGC was subsequently established and verified by comparison with the Nahum Experiment, Yoganandan experiment, and head-fall-to-ground (HFOG) experiments. Finally, regression models for the relationships between the HGC velocity and injury parameters of the brain tissue were constructed, and the optimal cutoff value of the HGC velocity was determined. Based on the results of the VVC accident reconstruction and case studies, these regression models and the cutoff value of the HGC velocity can accurately determine the severity of head injuries in pedestrians.

Indexed as

Accidents, TrafficCraniocerebral TraumaAdultChinaComputer SimulationEast Asian PeopleFinite Element AnalysisHumansMalePedestriansFinite element simulationHead-ground collisionHead injuriesVehicle-vulnerable road user accident

Identifiers

PMID40399369
PMCPMC12095569

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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.