Evidence map›Paper›PMID 41530650›Full record

ArticleAnnals of biomedical engineering2026

Investigating the Influence of Limited CT Scan Coverage Using an Automated Workflow with Biofidelic Sideways Fall FE Models.

Alexander Baker, Ingmar Fleps, Fang-Chi Hsu, Pierre Guy, Peter Cripton, Stephen J Ferguson, Benedikt Helgason

Abstract read
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In one paragraph

Article in Annals of biomedical engineering, 2026. 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

7 authors.

Alexander BakerLaboratory for Orthopaedic Technology, Department of Health Sciences and Technology, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland. alexander.baker@hest.ethz.ch.ORCID http://orcid.org/0000-0003-4150-6924
Ingmar FlepsLaboratory for Orthopaedic Technology, Department of Health Sciences and Technology, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland.
Fang-Chi HsuBiostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Pierre GuyCentre for Aging SMART, University of British Columbia, Vancouver, British Columbia, Canada.
Peter CriptonCentre for Aging SMART, University of British Columbia, Vancouver, British Columbia, Canada.
Stephen J FergusonLaboratory for Orthopaedic Technology, Department of Health Sciences and Technology, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland.
Benedikt HelgasonLaboratory for Orthopaedic Technology, Department of Health Sciences and Technology, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland.

Funding

Personalized Health & Related Technologies 2018-325Personalized Health & Related Technologies 2018-430
6 · The paper itself

Abstract

Sophisticated biofidelic finite element (FE) models of sideways falls are an emerging tool for predicting hip fracture risk. We adapted an existing experimental setup for in-silico trials by creating an automated workflow to build FE models of the experiment and characterizing it with respect to the effects of limited CT scan coverage. Limited CT scan coverage was simulated by shortening the femur (25-175mm distal to the greater trochanter), misaligning the femur up to 5 degrees, and using a morphed template pelvis. We compared impact force error (E

Indexed as

Accidental FallsFemurHip FracturesModels, BiologicalTomography, X-Ray ComputedFinite Element AnalysisHumansAutomationComputed tomographyFemur strengthFinite element modelingFragility fractureOsteoporosisSideways fall

Identifiers

PMID41530650

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.