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Record W2779199998

DOES IN VIVO LIGAMENT FORCE PASS THROUGH LIGAMENT INSERTIONS?: A ROBOTIC STUDY

2014· article· en· W2779199998 on OpenAlexvenueno aff
Nathan Quinn, Mohammad Atarod, Nigel G. Shrive

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2014
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsLigamentKinematicsAnterior cruciate ligamentStifle jointAnatomyFemurTibiaIn vivoSagittal planeMedial collateral ligamentOsteoarthritisAnkleCadaverCruciate ligamentMedicineBiologySurgeryPhysics
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Post traumatic osteoarthritis (PTOA) is a subset of osteoarthritis that arises after traumatic joint injury. Approximately half of patients suffering from anterior cruciate ligament (ACL) injuries have been shown to progress towards PTOA within fifteen years[1]. One of the areas of research for PTOA is examining the biomechanical changes following an ACL injury. A recent study[2] examined the effects of ACL transection (ACLx) in an ovine model. In vivo stifle joint motions were recorded, and recreated in vitro using a novel robotic testing platform. Joint and tissue loads were simultaneously recorded during reproduction of in vivo joint motion[2]. This novel approach was shown to be able to reproduce in vivo motions within less than 0.1° and 0.1mm[2]. The main objective of the present study was to determine if in vivo ligament forces, obtained using the principle of superposition, pass through respective ligament insertional footprints. This study evaluated the accuracy of the measured ligament forces, as they should pass through their bony insertions. This accuracy was examined for medial and lateral collateral ligaments (MCL and LCL), and posterior cruciate ligament (PCL) in ACLx sheep. METHODS Stifle joints from N=4 sheep that had been previously examined in ACLx kinematics studies were digitized using a coordinate measuring machine. The ligament insertional areas were thoroughly recreated in 3D space, along with the spatial position of the kinematic measurement device. Anatomic coordinate systems were defined on the femur and the tibia. These data were then exported to MATLAB scripts for further analysis. The ligament insertional areas were approximated as planes using least squares regression technique. The intersection of the ligament force vectors with the plane approximations were then calculated. The direction of a ligament force was deemed accurate if the distance between the calculated intersection and footprint centroid was within a determined threshold. A preliminary threshold was calculated as the maximum distance between the centroid and measured points of the footprint. RESULTS Analysis of the data indicated that the technique increases in accuracy for high ligament loads (Figure 1). That is to say as the ligament loads increase, the intersection-centroid distance is reduced. For lower ligament loads (mainly within the “toe” region of the ligament force-displacement behavior), however, the method requires further refinement. DISCUSSION AND CONCLUSIONS This study indicated that the method requires further refinement based on the limitations induced currently. Preliminary data shows that accuracy increases as ligament load magnitudes increase as well. Finding the intersections on plane approximations of the insertional footprints can lead to very large error when the planes approach a parallel state. In these states, a small angular change in force direction can cause a very large change in intersection-centroid distance. Each ligament and its insertions have different functions and geometries, and as such require separate limitations. Future studies would require modifications to the analysis method to account for the limitations involved with the current method.

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 score0.723
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.074
GPT teacher head0.409
Teacher spread0.334 · 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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