Evidence map›Paper›PMID 33030199›Full record

ArticleJournal of biomechanical engineering2021

Predictions of Anterior Cruciate Ligament Dynamics From Subject-Specific Musculoskeletal Models and Dynamic Biplane Radiography.

James P Charles, Freddie H Fu, William J Anderst

Abstract read
In one paragraph

Article in Journal of biomechanical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. From fibre to function: are we accurately representing muscle architecture and performance?Biological reviews of the Cambridge Philosophical Society · 2022
    Review
  4. In search of a gold standard for objective clinical outcome: using dynamic biplane radiography to measure knee kinematics.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2022
    Article
  5. 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

3 authors.

James P CharlesEvolutionary Morphology and Biomechanics Lab, Musculoskeletal Biology, University of Liverpool, Liverpool L7 8TX, UK; Biodynamics Lab, Department of Orthopaedic Surgery, University of Pittsburgh, Pittsburgh, PA 15260.
Freddie H FuBiodynamics Lab, Department of Orthopaedic Surgery, University of Pittsburgh, Pittsburgh, PA 15260.
William J AnderstBiodynamics Lab, Department of Orthopaedic Surgery, University of Pittsburgh, Pittsburgh, PA 15260.

Funding

A Probabilistic Pose Estimation Algorithm for 3D Motion Capture DataR44HD066831 · NICHD · C-MOTION, INC. · PI SELBIE, W. SCOTT · 2016 to 2017
$958k
NICHD NIH HHS R44 HD066831
6 · The paper itself

Abstract

In vivo knee ligament forces are important to consider for informing rehabilitation or clinical interventions. However, they are difficult to directly measure during functional activities. Musculoskeletal models and simulations have become the primary methods by which to estimate in vivo ligament loading. Previous estimates of anterior cruciate ligament (ACL) forces range widely, suggesting that individualized anatomy may have an impact on these predictions. Using ten subject-specific (SS) lower limb musculoskeletal models, which include individualized musculoskeletal geometry, muscle architecture, and six degree-of-freedom knee joint kinematics from dynamic biplane radiography (DBR), this study provides SS estimates of ACL force (anteromedial-aACL; and posterolateral-pACL bundles) during the full gait cycle of treadmill walking. These forces are compared to estimates from scaled-generic (SG) musculoskeletal models to assess the effect of musculoskeletal knee joint anatomy on predicted forces and the benefit of SS modeling in this context. On average, the SS models demonstrated a double force peak during stance (0.39-0.43 xBW per bundle), while only a single force peak during stance was observed in the SG aACL. No significant differences were observed between continuous SG and SS ACL forces; however, root mean-squared differences between SS and SG predictions ranged from 0.08 xBW to 0.27 xBW, suggesting SG models do not reliably reflect forces predicted by SS models. Force predictions were also found to be highly sensitive to ligament resting length, with ±10% variations resulting in force differences of up to 84%. Overall, this study demonstrates the sensitivity of ACL force predictions to SS anatomy, specifically musculoskeletal joint geometry and ligament resting lengths, as well as the feasibility for generating SS musculoskeletal models for a group of subjects to predict in vivo tissue loading during functional activities.

Indexed as

Anterior Cruciate LigamentAdultBiomechanical PhenomenaFemaleHumansKnee JointMaleMechanical PhenomenaModels, BiologicalPatient-Specific ModelingRadiographyjoint reaction analysiskneeligament forcesimulationsstatic optimization

Identifiers

PMID33030199
PMCPMC7871995

What OpenQuestion holds

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Registered trials

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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.