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Record W2170669804 · doi:10.1243/09544119jeim180

Mechanics of the passive knee joint. Part 1: The role of the tibial articular surfaces in guiding the passive motion

2006· article· en· W2170669804 on OpenAlexaff
Shahram Amiri, D Cooke, Il Yong Kim, U.P. Wyss

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsRotation (mathematics)Translation (biology)Motion (physics)Knee JointMedial meniscusMeniscusBiomechanicsAnatomyGeometryPhysicsMechanicsMedicineMathematicsClassical mechanicsOsteoarthritisSurgeryChemistry

Abstract

fetched live from OpenAlex

The motion of the unloaded knee is associated with tibial internal rotation and femoral posterior translation. Although it is known that the passive motion is the result of the interaction between the articular surfaces and the ligaments, the mechanism through which the particular pattern of motion is guided is not completely understood. The goal of this study was to focus on the tibial geometry and to identify the roles that its geometric features have in guiding the passive knee motion. The method used in this study simplified the geometry of the tibial plateaux and the menisci into basic features that could be eliminated individually. The generated tibial geometry was implemented in a computer model to simulate the passive motion. Different parts of the geometry were eliminated individually and the comparison between the simulation results was used to identify the role that each part of the geometry had in guiding the passive motion. The medial meniscus was found as the feature that promoted the tibial internal rotation and restrained the femoral posterior translation. The lateral meniscus and the medial aspect of the tibial eminence, on the other hand, were found as the elements that confined the tibial internal rotation.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.224
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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

Citations27
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in MedicineSame topicKnee injuries and reconstruction techniquesFrench-language works237,207