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Record W4415570976 · doi:10.1302/1358-992x.2025.11.018

A COMPARISON OF HOMOGENOUS VERSUS PHYSIOLOGICAL TRABECULAR BONE PROPERTIES IN THE FINITE ELEMENT EVALUATION OF IMPLANT PERFORMANCE

2025· article· en· W4415570976 on OpenAlexaff
D. A. Cunningham, G. Daniel G. Langohr, George S. Athwal, James A. Johnson

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsHand and Upper Limb Clinic
Fundersnot available
KeywordsFinite element methodCancellous boneBiomechanicsImplantTrabecular boneCompression (physics)Fixation (population genetics)

Abstract

fetched live from OpenAlex

Finite element (FE) methods of evaluating implant-bone behaviours have gained popularity due to their ability to estimate postoperative implant performance throughout the structure. However, although homogenous models for cancellous bone have been shown to satisfactorily replicate the biomechanics of human bone in compression test models, no studies have evaluated the efficacy of these models in a computational evaluation of the bone-implant construct. Accordingly, this finite element analysis investigated the effect of utilizing patient-specific trabecular bone material properties in comparison to using homogenous models mimicking commonly used polyurethane foam surrogates. We employed a stemless humeral component that relies primarily on cancellous fixation as used in total shoulder arthroplasty as our model. Five CT-derived (sex: male, age: 67 ± 20 years (mean ± standard deviation), mass: 72 ± 11 kg, height: 175 ± 3.1 cm) three-dimensional models of the proximal humerus were created from CT data using Mimics (Materialise, Leuven, Belgium). A generic implant was implanted into humeral models and assigned material properties using the Morgan et al [1] Young's modulus-bone density relationship using Mimics. Loads derived from previously published Orthoload [2] in-vivo telemetrized implant data, replicating a 30° abduction motion, were applied to each model. Each model was then reassigned new trabecular bone materials, representing the material properties of commonly used polyurethane foams [35PCF, 30PCF, 25PCF, 20PCF, 17PCF, 15PCF, 12PCF]. In addition to the CT-modelled homogenous bone models, a homogenous structure, representing a standard 13 cm x 18 cm x 4 cm 17 PCF foam block, was also tested under the same loading conditions as were applied to the humeral models. Maximum bone-implant distractions were the outcome variable employed. In all cases, increasing foam PCF decreased bone-implant relative distraction (p < 0.001) (Figure 1). When comparing foam material bone models to the CT-derived material models, bone-implant distractions after loading were most similar in the 17 PCF polyurethane foam homogenous models (CT-Derived Material: 18.78 ± 11.35 µm, 17 PCF Material: 18.10 ± 10.15 µm). Implant performance in the homogenous models was highly dependent on patient cortical bone morphology, with the 17 PCF CT-Models exhibiting higher average bone-implant relative distractions (18.10 µm) than their 17 PCF Foam Block counterpart (11.33 µm) (Figure 2). Simulated implant behaviour is dependent on the modelled trabecular bone material. When investigating macro-scale cancellous-base fixation behaviour, it is likely important to ensure that the selection of homogenous trabecular bone modulus approximates patient-specific bone properties and geometry. Moreover, employment of the outer cortical structure also has implications with regard to the response of the trabecular bed to loading of the implant. Further investigation into the effect of using synthetic bone models during experimental testing is suggested. For any figures or tables, please contact the authors directly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.331
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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