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Record W3081386453 · doi:10.1123/jab.2019-0335

Development of a Full Flexion 3D Musculoskeletal Model of the Knee Considering Intersegmental Contact During High Knee Flexion Movements

2020· article· en· W3081386453 on OpenAlexaff
David C. Kingston, Stacey M. Acker

Bibliographic record

VenueJournal of Applied Biomechanics · 2020
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of SaskatchewanUniversity of Waterloo
Fundersnot available
KeywordsSquatting positionKnee flexionContact forceKnee JointOrthodonticsMedicineShear forceBiomechanicsThighHeelAnatomyMaterials scienceSurgeryPhysics

Abstract

fetched live from OpenAlex

A musculoskeletal model of the right lower limb was developed to estimate 3D tibial contact forces in high knee flexion postures. This model determined the effect of intersegmental contact between thigh-calf and heel-gluteal structures on tibial contact forces. This model includes direct tracking and 3D orientation of intersegmental contact force, femoral translations from in vivo studies, wrapping of knee extensor musculature, and a novel optimization constraint for multielement muscle groups. Model verification consisted of calculating the error between estimated tibial compressive forces and direct measurements from the Grand Knee Challenge during movements to ∼120° of knee flexion as no high knee flexion data are available. Tibial compression estimates strongly fit implant data during walking (R2 = .83) and squatting (R2 = .93) with a root mean squared difference of .47 and .16 body weight, respectively. Incorporating intersegmental contact significantly reduced model estimates of peak tibial anterior-posterior shear and increased peak medial-lateral shear during the static phase of high knee flexion movements by an average of .33 and .07 body weight, respectively. This model supports prior work in that intersegmental contact is a critical parameter when estimating tibial contact forces in high knee flexion movements across a range of culturally and occupationally relevant postures.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.207
Teacher spread0.193 · 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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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Same venueJournal of Applied BiomechanicsSame topicMuscle activation and electromyography studiesFrench-language works237,207