CAN KNEE MOTION PATTERNS BE USED AS A PREDICTIVE TOOL FOR IMPLANT STATUS AND LONGEVITY
Notice bibliographique
Résumé
Introduction A thorough understanding of wear patterns and failure mechanisms of TKA components in the context of pre-revision knee kinematics is advantageous for component designers, manufacturers and surgeons alike. Traditional gait analysis provides an experimental technique to determine in vivo kinematics but is often limited by its cumbersome nature, infrastructure intensiveness and time. The recent introduction of the KneeKG (Emovi Inc, Canada) as a stand-alone knee motion tracking system which uses infrared technology provides a great opportunity to quickly, easily and routinely monitor patients at the clinical level, especially those being revised for component failure. This pilot study was conducted to examine pre-revision knee kinematics and subsequent wear patterns and failure mechanisms observed on the UHMWPE inserts upon retrieval in a cohort of TKA revision patients. We hypothesize that motion patterns can provide surgeons a unique insight into the status of the UHMWPE insert and implant longevity. Methods Patients requiring revision due to failure of the UHMWPE insert were recruited in this study after institutional ethical approval and written informed consent of the patients was obtained. Motion of the affected knee was quantified using a stand-alone infrared tracking system (KneeKG, Emovi Inc, Canada) whilst the patient was walking on a treadmill. All analyses were conducted within our institutional Physiotherapy Department. The KneeKG system is composed of passive motion sensors fixed on a validated knee harness, an infrared motion capture system (Polaris Spectra, Northern Digital Inc, USA) and a computer equipped with the Knee3D software suite (Emovi). Following application of the KneeKG trackers a calibration procedure was performed to identify joint centres and define a coordinate system on each body segment. After a treadmill habituation period of between 6 and 10 min, a trial was then conducted at the patient9s comfortable treadmill gait speed over 45 sec. Averaged clinical rotations and translations of the tibia as a function of gait cycle were output by the system, and a report highlighting and detailing biomechanical deficiencies as compared to a database of normal controls automatically generated. Following the scheduled revision surgery the retrieved components were formalin-fixed and brought to our laboratory for a routine retrieval workup. All revisions were performed by a single surgeon. Components were analysed using optical and scanning electron microscopy techniques for regions of polishing, burnishing, pitting, delamination, deformation, scratching and embedded debris. Wear maps and scores were generated and correlated with pre-revision kinematics for each patient. Results The KneeKG was successfully applied to patients in this pre-revision scenario, requiring less than 30 minutes to complete per case. Variations in knee kinematics have been observed, and the analysis of retrieved components is ongoing. Discussion This study has demonstrated that knowledge of pre-revision knee kinematic patterns can provide a unique insight into wear and failure mechanisms of the UHMWPE liner. Whilst this study is currently limited by a relatively small sample size, recruitment is continuing with a view to the possible generation of odds ratios for UHMWPE insert failure mechanisms based on kinematic signatures.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».