Evidence map›Paper›PMID 33881704›Full record

ArticleMedical & biological engineering & computing2021

A finite element-guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions.

P R Berthelson, P Ghassemi, J W Wood, G G Stubblefield, A J Al-Graitti, M D Jones, M F Horstemeyer, S Chowdhury, R K Prabhu

Abstract read
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Article in Medical & biological engineering & computing, 2021. 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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0cells of the map it votes in
0citing papers in PubMed
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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

9 authors.

P R BerthelsonCenter for Advanced Vehicular Systems, Mississippi State University, 200 Research Blvd, Starkville, MS, 39759, USA.
P GhassemiDepartment of Mechanical and Aerospace Engineering, University at Buffalo, 246 Bell Hall, Buffalo, NY, 14260, USA.
J W WoodCenter for Advanced Vehicular Systems, Mississippi State University, 200 Research Blvd, Starkville, MS, 39759, USA.
G G StubblefieldCenter for Advanced Vehicular Systems, Mississippi State University, 200 Research Blvd, Starkville, MS, 39759, USA.
A J Al-GraittiSchool of Engineering, Cardiff University, Cardiff, Wales, CF10 3AT, UK.
M D JonesSchool of Engineering, Cardiff University, Cardiff, Wales, CF10 3AT, UK.
M F HorstemeyerCenter for Advanced Vehicular Systems, Mississippi State University, 200 Research Blvd, Starkville, MS, 39759, USA.
S ChowdhuryDepartment of Mechanical and Aerospace Engineering, University at Buffalo, 246 Bell Hall, Buffalo, NY, 14260, USA. soumacho@buffalo.edu.
R K PrabhuCenter for Advanced Vehicular Systems, Mississippi State University, 200 Research Blvd, Starkville, MS, 39759, USA.ORCID http://orcid.org/0000-0002-6073-4802

Funding

Engineer Research and Development Center (US) W912HZ-17-C-0021
6 · The paper itself

Abstract

A finite element (FE)-guided mathematical surrogate modeling methodology is presented for evaluating relative injury trends across varied vehicular impact conditions. The prevalence of crash-induced injuries necessitates the quantification of the human body's response to impacts. FE modeling is often used for crash analyses but requires time and computational cost. However, surrogate modeling can predict injury trends between the FE data, requiring fewer FE simulations to evaluate the complete testing range. To determine the viability of this methodology for injury assessment, crash-induced occupant head injury criterion (HIC

Indexed as

Accidents, TrafficCraniocerebral TraumaBiomechanical PhenomenaFinite Element AnalysisHeadHumansCrashworthinessHead Injury Criterion (HIC)Human body modelingMeta-modelingResponse surface

Identifiers

PMID33881704

What OpenQuestion holds

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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.