Evidence map›Paper›PMID 35119555›Full record

ArticleMedical & biological engineering & computing2022

Study of the significance of parameters and their interaction on assessing femoral fracture risk by quantitative statistical analysis.

Rabina Awal, Jalel Ben Hmida, Yunhua Luo, Tanvir Faisal

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Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Rabina AwalDepartment of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA, USA.
Jalel Ben HmidaDepartment of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA, USA.
Yunhua LuoDepartment of Mechanical Engineering, University of Manitoba, Winnipeg, MB, Canada.
Tanvir FaisalDepartment of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA, USA. tanvir.faisal@louisiana.edu.ORCID http://orcid.org/0000-0003-3265-7579

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early assessment of hip fracture helps develop therapeutic and preventive mechanisms that may reduce the occurrence of hip fracture. An accurate assessment of hip fracture risk requires proper consideration of the loads, the physiological and morphological parameters, and the interactions between these parameters. Hence, this study aims at analyzing the significance of parameters and their interactions by conducting a quantitative statistical analysis. A multiple regression model was developed considering different loading directions during a sideways fall (angle [Formula: see text] and [Formula: see text] on the coronal and transverse planes, respectively), age, gender, patient weight, height, and femur morphology as independent parameters and Fracture Risk Index (FRI) as a dependent parameter. Strain-based criteria were used for the calculation of FRI with the maximum principal strain obtained from quantitative computed tomography-based finite element analysis. The statistical result shows that [Formula: see text] [Formula: see text], age [Formula: see text], true moment length [Formula: see text], gender [Formula: see text], FNA [Formula: see text], height [Formula: see text], and FSL [Formula: see text] significantly affect FRI where [Formula: see text] is the most influential parameter. The significance of two-level interaction [Formula: see text] and three-level interaction [Formula: see text] shows that the effect of parameters is dissimilar and depends on other parameters suggesting the variability of FRI from person to person.

Indexed as

Hip FracturesAccidental FallsFemurFinite Element AnalysisHumansTomography, X-Ray ComputedFinite element analysisHip fractureMultiple regression analysisParametric quantitative analysis

Identifiers

PMID35119555

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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.