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Record W2095161481 · doi:10.1080/10255842.2012.739161

The effect of implant design of linked total elbow arthroplasty on stability and stress: a finite element analysis

2012· article· en· W2095161481 on OpenAlexaff
Ryan Willing, Graham J.W. King, James A. Johnson

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2012
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsSt Joseph's Health CareWestern University
Fundersnot available
Keywordsvon Mises yield criterionFinite element methodStructural engineeringElbowStress (linguistics)DurabilityBearing (navigation)Materials scienceJoint (building)Joint stabilityEngineeringComputer scienceComposite materialSurgeryMedicine

Abstract

fetched live from OpenAlex

Several linked total elbow arthroplasty designs exist, which function similar to a loose hinge joint. Constraint behaviour is an important design consideration, as it affects joint stability, or how much secondary [e.g. varus-valgus (VV)] motion is permitted. Implant durability is also a concern, as bearing failures have been reported. This finite element analysis investigates the constraint characteristics and ultra high molecular weight polyethylene bearing stresses of three linked elbow design concepts [cylindrical (CY), hourglass (HG) and concave cylinder (CC)]. The bearing of the CY design was subjected to elevated Von Mises stresses (2.1-5.4 times higher than the HG and CC designs) due to edge loading. The HG design maintained low stresses, but was unable to provide consistent VV stability. The CC design also maintained low stresses while providing consistent VV stability. These results suggest that CC designs may provide better stability characteristics and durability in vivo, compared to the other two designs.

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)0.003
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.023
GPT teacher head0.314
Teacher spread0.290 · 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

Citations17
Published2012
Admission routes1
Has abstractyes

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Same venueComputer Methods in Biomechanics & Biomedical EngineeringSame topicElbow and Forearm Trauma TreatmentFrench-language works237,207