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Record W2169183805 · doi:10.5277/abb130305

The Comparison of Density-Elastic Modulus Equations for the Distal Ulna at Multiple Forearm Positions: A Finite Element Study

2013· article· en· W2169183805 on OpenAlexaff
Mark Neuert, Rebecca L. Austman, Cynthia E. Dunning

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsWestern University
Fundersnot available
KeywordsUlnaForearmIsotropyRotation (mathematics)Materials scienceBendingFinite element methodModulusMathematical analysisStructural engineeringPhysicsMathematicsComposite materialGeometryAnatomyOpticsMedicineEngineering

Abstract

fetched live from OpenAlex

The accuracy of an empirically derived density-modulus equation for bone depends upon the loading conditions and anatomic site of bone specimens used for experimentation. A recent study used FE modeling to compare the ability of three density-modulus relationships to predict strain during bending in neutral forearm rotation in the distal ulna; however, due to the inhomogeneous nature of these FE models, the performance of each equation is not necessarily consistent throughout forearm rotation. This issue is addressed in the present study, which compares the performance of these equations in pronation and supination. Strain gauge data were collected at six discreet locations of six ulna specimens loaded in bending at 40° of pronation and supination. Three FE models of each specimen were made, one for each density-modulus relation, and the strain output compared to the experimental data. The equation previously shown to be most accurate in predicting ulnar strain in neutral forearm rotation was also most accurate in pronation and supination. These results identify this one equation as the most appropriate for future FE analysis of the ulna (including adaptive remodeling, and further show that isotropic and inhomogeneous FE bone models may provide consistent results in different planes of bending.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.335

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 head0.041
GPT teacher head0.288
Teacher spread0.247 · 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".

Quick stats

Citations5
Published2013
Admission routes1
Has abstractyes

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