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Evidence That In Vivo Wear Damage Alters Kinematics and Contact Stresses in a Total Knee Replacement

2010· article· en· W2117486636 on OpenAlexaff
John L. Williams, David Knox, Matthew G. Teeter, David W. Holdsworth, William M. Mihalko

Bibliographic record

VenueJournal of Long-Term Effects of Medical Implants · 2010
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsRobarts Clinical TrialsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsPolyethyleneBiomechanicsKinematicsContact mechanicsGait cycleTotal knee arthroplastyMaterials scienceTotal knee replacementOrthopedic surgeryImplantOsteoarthritisKnee JointOrthodonticsGaitMedicineBiomedical engineeringSurgeryComposite materialAnatomyStructural engineeringFinite element methodEngineeringPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Polyethylene wear after a total knee arthroplasty is inevitable. The effects of the wear particles on the surrounding soft tissue causing inflammatory responses and eventual aseptic loosening are well documented, but the biomechanical changes from polyethylene wear have been less understood. This study investigated how wear from a retrieved polyethylene insert from a total knee arthroplasty changed the kinematics and contact stresses. A cruciate-retaining total knee implant (Natural-Knee, Intermedics Orthopedics, Inc., Austin, Texas) was retrieved from a donor program. The polyethylene insert was then scanned and modeled. KneeSIM (LifeMOD/KneeSIM, San Clemente, California) was used to simulate one cycle of gait of three second duration (100% of cycle). A threefold increase in contact stress as well as resulting kinematic changes were seen when the model was used to compare the retrieved versus a modeled off-the-shelf new polyethylene insert. Total knee designs should take into account the wear patterns that result from years of use and how they may affect the biomechanics of the knee long term.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.027
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.321
Teacher spread0.301 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Long-Term Effects of Medical ImplantsSame topicTotal Knee Arthroplasty OutcomesFrench-language works237,207