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Record W4408975399 · doi:10.1016/j.jmbbm.2025.106986

Accurate measurement of a bone surrogate flexural rigidity in three- and four-point bending

2025· article· en· W4408975399 on OpenAlexafffund
Mahsa Zojaji, Baixuan Yang, Caitlyn J. Collins, Thomas D. Crenshaw, Heidi‐Lynn Ploeg

Bibliographic record

VenueJournal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials · 2025
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsQueen's University
FundersNational Center for Advancing Translational SciencesSchool of Graduate Studies, Queen's UniversityNatural Sciences and Engineering Research Council of CanadaQueen's University
KeywordsFlexural rigidityThree point flexural testFlexural strengthRigidity (electromagnetism)BendingPoint (geometry)Structural engineeringMathematicsOrthodonticsMaterials scienceGeometryEngineeringMedicine

Abstract

fetched live from OpenAlex

The mechanical assessment of long bones through bending is an established preclinical approach to evaluate the effectiveness of treatments for osteoporosis and fractures. Three- and four-point bending (3PB and 4PB) tests are the most common methods for mechanical characterization of long bones with Euler-Bernoulli (EB) theory to calculate of bone flexural rigidity (EI). Previous studies demonstrated that EB theory underestimates the EI of long bones due to its reliance on assumptions that are not entirely applicable to long bones. Therefore, the current study aimed to evaluate the factors that affect the percent error (PE) and bias stemming from the omission of contact and shear deflections in the EI estimation using mechanical testing and finite element analysis (FEA). The true EI of a porcine bone surrogate was used to quantify the percent error and bias of EI estimations from three deflection measurement methods and FEA, in 3PB and 4PB. The analysis confirmed that bending was the main component of total deflection, but only contributed to approximately 50 % and 65 % of the total deflection in 3PB and 4PB, respectively. The combined shear and indentation deflections accounted for the remainder of the total deflection. The FEA aligned with 3PB and 4PB tests with less than 10 % deviation. Underestimation of EI was largest with deflection measurements taken from the machine crosshead (PE 74 % in 3PB and 71 % in 4PB). However, these underestimations improved notably when indentation and shear deflections were considered (PE 42 % in 3PB and 39 % in 4PB). The deflection measurements from extensometer and digital image correlation (DIC) underestimated the EI by 66 % and 71 % in 3PB, and 57 % and 59 % in 4PB. When corrected for shear and indentation deflections, the 3PB PE reduced to 16 % and 14 %, respectively. In 4PB, PE reduced to, 10 % and 7 %, respectively, demonstrating the advantage of the 4PB test configuration over 3PB. The bias resulting from shear deflection was not consistent across deflection measurement methods; and therefore, cannot be generalized with a constant bias correction. The current study highlighted that a slight error in deflection measurement can lead to a significant inaccuracy in EI measurements. This sensitivity comes from the hyperbolic relationship between EI and deflection which not only depends on the ratio of support span to diameter of the specimen but also the test configuration and the ratio of the elastic to shear modulus of specimen. In other words, the PE from neglecting shear effects increases as the specimen EI increases. Accuracy with less than 10 % PE in EI estimations can be achieved by: 1. taking deflection measurements with extensometers, DIC, or FEA; 2. testing in 4PB instead of 3PB; and, 3. correcting for indentation and shear deflections.

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)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.287
Teacher spread0.253 · 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.

Study designBench or experimental
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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

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