Evidence map›Paper›PMID 32219092›Full record

ArticleFrontiers in bioengineering and biotechnology2020

Development of a Canine Rigid Body Musculoskeletal Computer Model to Evaluate Gait.

Nathan P Brown, Gina E Bertocci, Gregory J R States, Gwendolyn J Levine, Jonathan M Levine, Dena R Howland

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

7 citing papers in PubMed, 15 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Nathan P BrownCanine Rehabilitation and Biomechanics Laboratory, Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, United States.
Gina E BertocciCanine Rehabilitation and Biomechanics Laboratory, Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, United States.
Gregory J R StatesCanine Rehabilitation and Biomechanics Laboratory, Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, United States.
Gwendolyn J LevineDepartment of Veterinary Pathobiology, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, United States.
Jonathan M LevineDepartment of Small Animal Clinical Sciences, Veterinary Medical Teaching Hospital, Texas A&M University, College Station, TX, United States.
Dena R HowlandKentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, United States.
University of Louisville · USTexas A&M University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundKinematic and kinetic analysis have been used to gain an understanding of canine movement and joint loading during gait. By non-invasively predicting muscle activation patterns and forces during gait, musculoskeletal models can further our understanding of normal variability and muscle activation patterns and force profiles characteristic of gait.

methodsPelvic limb kinematics and kinetics were measured for a 2 year old healthy female Dachshund (5.4 kg) during gait using 3-D motion capture and force platforms. A computed tomography scan was conducted to acquire pelvis and pelvic limb morphology. Using the OpenSim modeling platform, a bilateral pelvic limb subject-specific rigid body musculoskeletal computer model was developed. This model predicted muscle activation patterns, muscle forces, and angular kinematics and joint moments during walking.

resultsGait kinematics determined from motion capture matched those predicted by the model, verifying model accuracy. Primary muscles involved in generating joint moments during stance and swing were predicted by the model: at mid-stance the adductor magnus et brevis (peak activation 53.2%, peak force 64.7 N) extended the hip, and stifle flexor muscles (biceps femoris tibial and calcaneal portions) flexed the stifle. Countering vertical ground reaction forces, the iliopsoas (peak activation 37.9%, peak force 68.7 N) stabilized the hip in mid-stance, while the biceps femoris patellar portion stabilized the stifle in mid-stance and the plantar flexors (gastrocnemius and flexor digitorum muscles) stabilized the tarsal joint during early stance. Transitioning to swing, the iliopsoas, rectus femoris and tensor fascia lata flexed the hip, while in late swing the adductor magnus et brevis impeded further flexion as biceps femoris tibial and calcaneal portions stabilized the stifle for ground contact.

conclusionThe musculoskeletal computer model accurately replicated experimental canine angular kinematics associated with gait and was used to predict muscle activation patterns and forces. Thus, musculoskeletal modeling allows for quantification of measures such as muscle forces that are difficult or impossible to measure

Indexed as

biomechanicscaninecomputer modelgaitkinematicsmuscle activationmuscle forcepelvic limb

Identifiers

PMID32219092
PMCPMC7079575
OpenAlexW3011409016

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.