Notice bibliographique
Résumé
Over the next two decades, the number of people living with stroke in Canada is expected to rise significantly-from 405,000 in 2013 to an estimated 726,000 in 2038-as a result of aging, reduced stroke mortality, and population growth. 2 Although stroke mortality is decreasing, stroke survivors are living with disabilities; 2, 3 40% of them will have moderate to severe impairments, 4 and as much as 40% of them will have limited to no walking ability.5 Thus, the recovery of functional walking capacity is a primary goal of post-stroke survivors as well as neurorehabilitation therapists.6 The past 3 decades have seen several technological advancements in the rehabilitation of locomotion with the introduction of body weight-supported treadmill training, 7 robotic devices such as the Lokomat, 6 and human augmentation through exoskeleton devices.8 Simultaneously, virtual reality (VR)-the ability to simulate real-world objects and events-and virtual environments-providing interactive, context-specific environments simulating everyday events-have been developed as rehabilitation tools, 9 enabling the manipulation of sensory-motor experiences in a safe and progressive environment.Richards and colleagues 1 have developed a novel rehabilitation tool consisting of a VR-coupled treadmill system that provides the person post-stroke with a training environment that simulates both the visual and the physical demands of a real-life complex environment, simultaneously challenging the sensorymotor and cognitive components of locomotion.The decision to investigate this novel intervention with a person 32 months post-stroke is a welcome addition to the evidence base, which is limited for the population with chronic stroke, 10 and it demonstrates clinical improvements that identify that ongoing recovery is possible in this population.Unfortunately, access to neuro-rehabilitation is scarce for community-dwelling chronic stroke survivors.This proof-of-principle study highlights the many challenges inherent in neuro-rehabilitation research-in particular, the interface with clinical practice.The research design was creative and thorough, using a factorial case study design.The participant was a community-dwelling independent ambulator with no significant cognitive, perceptual, or communication difficulties or other comorbidities that affected his mobility.This clinical presentation will likely be difficult to replicate with larger numbers of post-stroke participants because approximately 50%-70% of stroke survivors present with cognitive deficits.11 In addition, the expanding aging population in Canada brings clinical complexity because of the increased likelihood of the coexistence of two or more chronic conditions.12 A major challenge for all health care providers is managing multi-morbidity in their scope of practice.12 Likewise, access to and use of expensive, complex technologies requires careful consideration of access to capital and training investments.Understanding the facilitators of, and barriers to, the use of complex technology in clinical practice is essential.13 Time is critical in clinical practice, and, therefore, the challenge for the clinician is weighing the training time required to use the technology effectively and safely, and the time required for appropriate set-up, along with the potential benefits and risks of using
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 | 0,004 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,049 | 0,045 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 0,021 |
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 ».