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Record W1558025036 · doi:10.1002/jor.22472

Evaluation of knee joint muscle forces and tissue stresses‐strains during gait in severe OA versus normal subjects

2013· article· en· W1558025036 on OpenAlexafffund
Malek Adouni, A. Shirazi‐Adl

Bibliographic record

VenueJournal of Orthopaedic Research® · 2013
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOsteoarthritisGait cycleGaitMedicineKnee JointContact forceWeight-bearingAnterior cruciate ligamentPhysical medicine and rehabilitationCartilageGait analysisBiomechanicsKinematicsPhysical therapyAnatomySurgery

Abstract

fetched live from OpenAlex

Osteoarthritis (OA) is the leading cause of pain and disability in the elderly with the knee being the most affected weight bearing joint. We used a musculoskeletal biomechanical model of the lower extremity including a detailed validated knee joint finite element model to compute lower extremity muscle forces and knee joint stresses-strains during the stance phase of gait. The model was driven by gait data on OA patients, and results were compared with those of the same model driven by data on normal controls. Additional analyses were performed with altered cartilage-menisci properties to evaluate the effects of deterioration during OA. In OA patients compared to normal subjects, muscle forces dropped at nearly all stance periods except mid-stance. Force in the anterior cruciate ligament remained overall the same. Total contact forces-stresses deceased by about 25%. Alterations in properties due to OA had negligible effects on muscle forces, but increased contact areas and cartilage strains and reduced contact pressures. Reductions in contact stresses and increases in tissue strains and transfer of load via menisci are partly due to the altered kinetics-kinematics of gait and partly due to deterioration in cartilage-menisci properties in OA patients.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.846
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.355
Teacher spread0.278 · 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

Citations71
Published2013
Admission routes2
Has abstractyes

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