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Computational Poromechanics of Human Knee Joint

2012· article· en· W2055365823 on OpenAlexaff
Mojtaba Kazemi, LePing Li

Bibliographic record

VenueJournal of Physics Conference Series · 2012
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPoromechanicsKnee JointJoint (building)Materials scienceCartilageBiomedical engineeringFluid dynamicsArticular cartilageInterstitial fluidAnatomyMechanicsMedicineOsteoarthritisStructural engineeringComposite materialPorosityPorous mediumEngineeringSurgeryPathology

Abstract

fetched live from OpenAlex

Extensive computer modeling has been performed in the recent decade to investigate the mechanical response of the healthy and repaired knee joints. Articular cartilages and menisci have been commonly modeled as single-phase elastic materials in the previous 3D simulations. A comprehensive study considering the interplay of the collagen fibers and fluid pressurization in the tissues in situ remains challenging. We have developed a 3D model of the human knee accounting for the mechanical function of collagen fibers and fluid flow in the cartilages and menisci. An anatomically accurate structure of the human knee was used for this purpose including bones, articular cartilages, menisci and ligaments. The fluid pressurization in the femoral cartilage and menisci under combined creep loading was investigated. Numerical results showed that fluid flow and pressure in the tissues played an important role in the mechanical response of the knee joint. The load transfer in the joint was clearly seen when the fluid pressure was considered.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.241
Teacher spread0.201 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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