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Enregistrement W2779199998

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

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

Notice bibliographique

RevueJournal of undergraduate research in Alberta · 2014
Typearticle
Langueen
DomaineMedicine
ThématiqueShoulder Injury and Treatment
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLigamentKinematicsAnterior cruciate ligamentStifle jointAnatomyFemurTibiaIn vivoSagittal planeMedial collateral ligamentOsteoarthritisAnkleCadaverCruciate ligamentMedicineBiologySurgeryPhysics
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,723
Score d'incertitude au seuil0,558

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,074
Tête enseignante GPT0,409
Écart entre enseignants0,334 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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