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Record W3038185117 · doi:10.1115/1.4047679

Misalignment Error in Cancellous Bone Apparent Elastic Modulus Depends on Bone Volume Fraction and Degree of Anisotropy

2020· article· en· W3038185117 on OpenAlexafffund
Matthew B. L. Bennison, A.K. Pilkey, W. Brent Lievers

Bibliographic record

VenueJournal of Biomechanical Engineering · 2020
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsQueen's UniversityLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCancellous boneAnisotropyMaterials scienceVolume fractionElastic modulusDegree (music)ModulusBiomedical engineeringComposite materialOpticsPhysicsAnatomyAcousticsEngineering

Abstract

fetched live from OpenAlex

Cancellous bone is an anisotropic structure with architectural and mechanical properties that vary due to both skeletal site and disease state. This anisotropy means that, in order to accurately and consistently measure the mechanical properties of cancellous bone, experiments should be performed along the primary mechanical axis (PMA), that is, the orientation in which the mechanical properties are at their maximum value. Unfortunately, some degree of misalignment will always be present, and the magnitude of the resulting error is expected to be architecture dependent. The goal of this work is to quantify the dependence of the misalignment error, expressed in terms of change in apparent elastic modulus (ΔE), on both the bone volume fraction (BV/TV) and the degree of anisotropy (DA). Finite element method (FEM) models of bovine cancellous bone from five different skeletal sites were created at 5 deg and 20 deg from the PMA determined for each region. An additional set of models was created using image dilation/erosion steps in order to control for BV/TV and better isolate the effect of DA. Misalignment error was found to increase with increasing DA and decreasing BV/TV. At 5 deg misaligned from the PMA, error is relatively low (<5%) in all cases but increases to 8-24% error at 20 deg. These results suggest that great care is needed to avoid introducing misalignment error into experimental studies, particularly when studying regions with high anisotropy and/or low bone volume fraction, such as vertebral or osteoporotic bone.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.038
GPT teacher head0.295
Teacher spread0.257 · 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 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

Citations5
Published2020
Admission routes2
Has abstractyes

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