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Record W2083966332 · doi:10.1115/imece2004-59873

Finite Element Dynamic Response Analysis of the Human Knee Joint

2004· article· en· W2083966332 on OpenAlexafffund
T. Wayne Pfeiler, Mehran Kasra, A. Shirazi-Adl, Harold E. Cates

Bibliographic record

VenueAdvances in Bioengineering · 2004
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKnee JointOsteoarthritisFinite element methodJoint (building)StiffnessPhysical medicine and rehabilitationStructural engineeringComputer scienceMedicineEngineeringSurgeryPathology

Abstract

fetched live from OpenAlex

Mechanical factors play an important role in the etiology of knee injuries and diseases such as osteoarthritis (OA). While performing daily, occupational, and sport activities, the joint is subjected to dynamic loads such as vibration and multiple impacts. According to an individual’s age, fitness, and weight, these activities may cause the joint load, stiffness, and damping to reach critical limits initiating or accelerating different knee disorders such as osteoarthritis [Wolfe et al., 1996]. Computational models of the knee have been developed over the past several decades. However, these models leave much to be desired, since they often over-simplify the geometry and material properties of the knee. Several two dimensional models have been created [Gill et al., 1996]. Three-dimensional analytical studies have become more common in recent years, and typically model the tibiofemoral joint [Abdel-Rahman et al., 1993; Blankevoort et al, 1991; Wisman et al., 1980]. These studies typically model only surfaces and neglect the effect of ligaments and menisci/cartilage.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.274
Teacher spread0.266 · 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

Citations0
Published2004
Admission routes2
Has abstractyes

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