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Record W4390341293 · doi:10.1080/10255842.2023.2298371

Biomechanical effects of different loads and constraints on finite element modeling of the humerus

2023· article· en· W4390341293 on OpenAlexaff
Sabrina Islam, Kunal Manoj Gide, Emil H. Schemitsch, Habiba Bougherara, Radovan Zdero, Z. Shaghayegh Bagheri

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2023
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsToronto Rehabilitation InstituteUniversity Health NetworkToronto Metropolitan UniversityVictoria HospitalWestern University
Fundersnot available
KeywordsFinite element methodHumerusStructural engineeringBiomechanicsOrthodonticsComputer scienceEngineeringMedicineAnatomy

Abstract

fetched live from OpenAlex

Currently, there is no established finite element (FE) method to apply physiologically realistic loads and constraints to the humerus. This FE study showed that 2 'simple' methods involving direct head loads, no head constraints, and rigid elbow or mid-length constraints created excessive stresses and bending. However, 2 'intermediate' methods involving direct head loads, but flexible head and elbow constraints, produced lower stresses and bending. Also, 2 'complex' methods involving muscles to generate head loads, plus flexible head and elbow constraints, generated the lowest stresses and moderate bending. This has implications for FE modeling research on intact and implanted humeri.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.340
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2023
Admission routes1
Has abstractyes

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