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Record W2162824996 · doi:10.1115/sbc2011-53582

Quantification of In Vivo Laxity in the ACL and Individual Knee Joint Structures

2011· article· en· W2162824996 on OpenAlexaff
Lindsey Westover, Jessica Küpper, Janet L. Ronsky

Bibliographic record

VenueASME 2011 Summer Bioengineering Conference, Parts A and B · 2011
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsKnee JointAnterior cruciate ligamentLigamentJoint (building)BiomechanicsMedicineArthritisOrthodonticsOsteoarthritisJoint hypermobilityJoint capsuleCartilageDisplacement (psychology)AnatomySurgeryStructural engineeringPsychologyEngineeringPathology

Abstract

fetched live from OpenAlex

In biomechanical terms, passive joint laxity is a measure of joint movement within the constraints of ligaments, capsule, and cartilage [1] when an external force is applied to the joint during a state of muscular relaxation. Excessive knee joint laxity (reduced stiffness) can result from soft tissue injury, such as a ligament tear, or from genetic factors such as benign joint hypermobility syndrome, and can predispose the joint to instability including recurrent dislocations, and low-grade inflammatory arthritis [2]. A novel technique for in vivo measurement of 3D knee joint laxity using magnetic resonance (MR) imaging with a custom knee loading apparatus (KLA) has been developed in our research group [3]. Gross joint laxity is predicted based on joint displacement in response to an applied anterior tibial load. To better understand the link between laxity and instability, and to advance this technique for clinical applications, the laxity of individual joint structures, such as the anterior cruciate ligament (ACL) must be quantified.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.288

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 head0.082
GPT teacher head0.270
Teacher spread0.188 · 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
Published2011
Admission routes1
Has abstractyes

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Same venueASME 2011 Summer Bioengineering Conference, Parts A and BSame topicKnee injuries and reconstruction techniquesFrench-language works237,207