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Enregistrement W4392466898 · doi:10.3389/fspor.2024.1389519

Editorial: Multi-modal approaches to assess the impact of orthopaedic disease on lower extremity joint function and health

2024· editorial· en· W4392466898 sur OpenAlexaff
Malek Adouni, Yosra Cherni, Amir Esrafilian, Michael A. Samaan

Notice bibliographique

RevueFrontiers in Sports and Active Living · 2024
Typeeditorial
Langueen
DomaineMedicine
ThématiqueKnee injuries and reconstruction techniques
Établissements canadiensUniversité de Montréal
Organismes subventionnairesNational Institute on Aging
Mots-clésBiomechanicsModalPhysical medicine and rehabilitationMedicineJoint (building)Physical therapyEngineeringAnatomyStructural engineering

Résumé

récupéré en direct d'OpenAlex

Two of the papers within this research topic provided insight into lateral malleolar fractures and distal femoral fractures by integrating imaging-based techniques to develop more effective surgical interventions. The first fracture-related paper by Wang et al. delves into the intricate details of Type B lateral malleolar fractures. By employing a three-dimensional perspective, it provides a novel understanding of fracture apexes, which is crucial for developing more effective surgical treatments. This research not only contributes to the field of orthopedic surgery but also offers practical insights for clinicians dealing with such fractures. The second fracture-related paper by Chen et al. focused on identifying fracture lines and comminution zones in AO/OTA types 33A and 33C distal femoral fractures using three-dimensional computed tomography mapping. The distinct features observed in these fractures have significant implications for surgical approaches and internal fixation strategies. The findings, particularly the involvement of the medial femoral epiphysis in 33C fractures, provide valuable insights for selecting surgical approaches, optimizing internal fixation strategies, and guiding the placement of plates or bridging fixation for stabilizing the medial column. These results serve as essential guidelines for both surgical planning and biomechanical studies.A narrative review article by Keles et al. focused on the biomechanics of muscle-tendon units in the knee and ankle joints in the context of cerebral palsy. This comprehensive review shed light on the challenges in measuring and modeling muscle forces in people with cerebral palsy. The article also emphasizes the potential for advancements in clinical management and rehabilitation strategies for individuals with cerebral palsy. By bringing attention to these issues, the article emphasizes the potential for advancements in clinical management and rehabilitation strategies for individuals with cerebral palsy, an understudied patient population in the field of biomechanics.The next article by Chang et al. investigated the application of cutting-edge technology in sports science. By employing inertial measurement units driven by deep learning algorithms to clarify the stages of runner fatigue, hence it opens new avenues for research into athletic performance and injury prevention. This approach not only enhances our understanding of biomechanics and physiology in sports but also it shows how technological innovations can be connected in the field of orthopedic health.Advances in motion capture and musculoskeletal modeling have played a crucial role in the development and application of robotic technologies for rehabilitation. Lower limb exoskeletons are designed to assist human movement and enhance physiological performance. However, performance and control stability are affected by some model parameters and control algorithms. A manuscript by Gao et al. sought to utilize numerical optimization to adjust the stiffness and damping parameters of a rehabilitation robot to more optimally and accurately track the gait profile in the human-robot interaction.The evaluation of patellofemoral joint (PFJ) stress is important in understanding the mechanism of PFJ pain. Despite the obvious clinical need to assess PFJ stress, the article by Wang et al. highlights the absence of a definitive "gold standard" for assessment and ongoing efforts to enhance the accuracy of PFJ stress estimations. This article has identified and categorized the various methods used to estimate PFJ stress, the absence of a standardized assessment and the significance of precise evaluation in designing tissue-engineered constructs and evaluating patient recovery. The proposed evaluation scheme prompts future studies to focus on establishing modeling-based platforms for precise PFJ stress calculations, contributing to a deeper understanding of the mechanisms of PFJ pain and optimizing PFJ treatment programs.Falls are a major concern in the population as they are a leading cause of mortality. The ability to recover from a trip and regain balance, in order to prevent risk of falling, is not well understood. The paper by Namayeshi et al. utilized computational modeling to evaluate the role of the plantar flexor muscle in trip recovery during walking. This study demonstrated the significance of the ankle plantar flexor musculature in successful recovery after tripping during walking and suggests that plantar flexor muscle weakness is an important factor in the risk of tripping during walking. Together, all the articles within this research topic represent a multifaceted exploration of orthopedic health. These articles not only reflect the interdisciplinary nature of current orthopedic research but also underline the capability for integrating various scientific, technological and computational approaches to enhance our management of orthopedic diseases. This special issue thus serves as a valuable resource for researchers, clinicians, and practitioners in the field, offering insights into the complexities of joint function and health in orthopedic conditions.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,026
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,048

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,026
Méta-épidémiologie (sens strict)0,0050,001
Méta-épidémiologie (sens large)0,0050,004
Bibliométrie0,0040,002
Études des sciences et des technologies0,0020,002
Communication savante0,0060,004
Science ouverte0,0050,001
Intégrité de la recherche0,0130,014
Charge utile insuffisante (le modèle a refusé de juger)0,0140,011

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 Opus0,040
Tête enseignante GPT0,304
Écart entre enseignants0,264 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

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

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
Publié2024
Routes d'admission1
Résumé présentoui

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