Evidence map›Paper›PMID 33423096›Full record

ReviewJournal of bone and mineral metabolism2021

On challenges in clinical assessment of hip fracture risk using image-based biomechanical modelling: a critical review.

Yunhua Luo

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of bone and mineral metabolism, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 25% of its field
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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. 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

1 author at 1 institution in 1 country.

Yunhua LuoDepartment of Mechanical Engineering, University of Manitoba, 75A Chancellor's Circle, Winnipeg, MB, R3T 2N2, Canada. Yunhua.Luo@umanitoba.ca.ORCID http://orcid.org/0000-0003-0271-1319
University of Manitoba · CA

Funding

Natural Sciences and Engineering Research Council of Canada RGPIN-2019-05372
6 · The paper itself

Abstract

introductionHip fracture is a common health risk among elderly people, due to the prevalence of osteoporosis and accidental fall in the population. Accurate assessment of fracture risk is a crucial step for clinicians to consider patient-by-patient optimal treatments for effective prevention of fractures. Image-based biomechanical modeling has shown promising progress in assessment of fracture risk, and there is still a great possibility for improvement. The purpose of this paper is to identify key issues that need be addressed to improve image-based biomechanical modeling. MATERIALS AND

methodsWe critically examined issues in consideration and determination of the four biomechanical variables, i.e., risk of fall, fall-induced impact force, bone geometry and bone material quality, which are essential for prediction of hip fracture risk. We closely inspected: limitations introduced by assumptions that are adopted in existing models; deficiencies in methods for construction of biomechanical models, especially for determination of bone material properties from bone images; problems caused by separate use of the variables in clinical study of hip fracture risk; availability of clinical information that are required for validation of biomechanical models. RESULTS AND

conclusionsA number of critical issues and gaps were identified. Strategies for effectively addressing the issues were discussed.

Indexed as

Models, BiologicalAccidental FallsBiomechanical PhenomenaFinite Element AnalysisHip FracturesHumansRisk FactorsBiomechanical modelingFemur strengthHip fractureImpact forceRisk of fall

Identifiers

PMID33423096
OpenAlexW3119916988

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.