Evidence map›Paper›PMID 31471110›Full record

ArticleJournal of biomechanics2020

Musculoskeletal full-body models including a detailed thoracolumbar spine for children and adolescents aged 6-18 years.

Stefan Schmid, Katelyn A Burkhart, Brett T Allaire, Daniel Grindle, Dennis E Anderson

Abstract read
In one paragraph

Article in Journal of biomechanics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Machine learning outperforms anthropometric scaling in predicting muscle parameters and spinal loading: a subject-specific musculoskeletal modeling study.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Article
  3. Development and Assessment of a Novel Generic Finite Element Spine Model for Clinical Applications.International journal for numerical methods in biomedical engineering · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Computational modelling of the scoliotic spine: A literature review.International journal for numerical methods in biomedical engineering · 2021
    Review
  12. 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

5 authors.

Stefan SchmidBeth Israel Deaconess Medical Center, Center for Advanced Orthopaedic Studies, Boston, MA, United States; Harvard Medical School, Department of Orthopaedic Surgery, Boston, MA, United States; Bern University of Applied Sciences, Department of Health Professions, Division of Physiotherapy, Bern, Switzerland. Electronic address: stefanschmid79@gmail.com.
Katelyn A BurkhartBeth Israel Deaconess Medical Center, Center for Advanced Orthopaedic Studies, Boston, MA, United States; Massachusetts Institute of Technology, Harvard-MIT Health Sciences and Technology Program, Cambridge, MA, United States.
Brett T AllaireBeth Israel Deaconess Medical Center, Center for Advanced Orthopaedic Studies, Boston, MA, United States.
Daniel GrindleBeth Israel Deaconess Medical Center, Center for Advanced Orthopaedic Studies, Boston, MA, United States.
Dennis E AndersonBeth Israel Deaconess Medical Center, Center for Advanced Orthopaedic Studies, Boston, MA, United States; Harvard Medical School, Department of Orthopaedic Surgery, Boston, MA, United States.

Funding

Tech-DevelopmentP2CHD065690 · NICHD · STANFORD UNIVERSITY · PI DELP, SCOTT L · 2015 to 2019
$5.4M
NICHD NIH HHS P2C HD065690
6 · The paper itself

Abstract

Currently available musculoskeletal inverse-dynamics thoracolumbar spine models are entirely based on data from adults and might therefore not be applicable for simulations in children and adolescents. In addition, these models lack lower extremities, which are required for comprehensive evaluations of functional activities or therapeutic exercises. We therefore created OpenSim-based musculoskeletal full-body models including a detailed thoracolumbar spine for children and adolescents aged 6-18 years and validated by comparing model predictions to in vivo data. After combining our recently developed adult thoracolumbar spine model with a lower extremity model, children and adolescent models were created for each year of age by adjusting segmental length and mass distribution, center of mass positions and moments of inertia of the major body segments as well as sagittal pelvis and spine alignment based on literature data. Similarly, muscle strength properties were adjusted based on CT-derived cross-sectional area measurements. Simulations were conducted from in vivo studies reported in the literature involving children and adolescents evaluating maximum trunk muscle strength (MTMS), lumbar disc compressibility (LDC), intradiscal pressure (IDP) and trunk muscle activity (MA). Model predictions correlated highly with in vivo data (MTMS: r ≥ 0.82, p ≤ 0.03; LDC: r = 0.77, p < 0.001; IDP: r ≥ 0.78, p < 0.001; MA: r ≥ 0.90, p < 0.001), indicating suitability for the reasonably accurate prediction of maximal trunk muscle strength, segmental loading and trunk muscle activity in children and adolescents. When aiming at investigating children or adolescents with pathologies such as idiopathic scoliosis, our models can serve as a basis for the creation of deformed spine models and for comparative purposes.

Indexed as

Models, AnatomicAdolescentAdultChildFemaleHumansLumbar VertebraeMaleMusclesMuscle StrengthThoracic VertebraeTorsoWeight-BearingDevelopmentModelingOpenSimStatic optimizationValidation

Identifiers

PMID31471110
PMCPMC7315467

What OpenQuestion holds

Textmetadata
LicenceTDM
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.