Developing an ontology-based classification for mobility among individuals with acquired brain injury
Notice bibliographique
Résumé
Acquired brain injury (ABI), including traumatic brain injury (TBI) and stroke, is a leading cause of disability in Canada. Over 60% of the 1.5 million Canadians with ABI that go through the care continuum annually report ongoing restrictions in mobility and participation in societal roles. Planning rehabilitation intervention requires an understanding of the nature and severity of mobility challenges among individuals with ABI through a comprehensive evaluation of mobility. Thus, this PhD work comprises four studies, all addressing the global objective “to provide a common language and taxonomy of mobility to help compare and select mobility measures for clinical care and research among individuals with ABI (Stroke and TBI)”. The objective of Manuscript 1 was to synthesize the measurement properties, the interpretability, and the feasibility of mobility measures, from various sources of information (patients, clinicians, technology), through an umbrella review of published systematic reviews among individuals with ABI. Given that the umbrella review may not cover all measures that evaluate the determinants influencing mobility, focus group discussions were conducted among clinicians, individuals with ABI, and their caregivers. Thus, the objective of Manuscript 2 was to identify factors influencing mobility which need to be considered while evaluating mobility, and incorporating patients' needs and preferences into individualized care management plans, as perceived by clinicians, individuals with ABI, and their caregivers. Results of the focus groups identified the measures used in clinical practice and the determinants that influence mobility among individuals with ABI. Given that the care process emerged when we explored factors influencing mobility evaluation with clinicians, individuals with ABI, and their caregivers, Manuscript 3 aimed to explore the care experiences and service design related to rehabilitation for mobility and participation in the community among individuals with ABI, as perceived by clinicians, individuals with ABI, and their caregivers. Perspectives from clinicians, individuals with ABI, and their caregivers identified mobility factors related to service provisions, which are classified as environmental factors in the ICF that may improve mobility rehabilitation from the acute level of care to community re-integration among individuals with ABI. Manuscripts 1 and 2 synthesized critical information to define the breadth of mobility measures; Manuscripts 2 and 3 identified determinants that influence mobility, reflecting that mobility is a multidimensional construct affected by the interactions between Body Functions, Activity and Participation, and Contextual Factors. This complexity of measuring mobility, given that it is a multidimensional construct, requires robust strategies for organizing and effectively curating scientific knowledge to enable aggregation and comparison of findings across research studies. Natural language processing (NLP) is one approach that can be used to properly classify pre-defined content from mobility measures to understand knowledge evolution and correctly reflect and evolve our understanding of mobility. Thus, the objective of Manuscript 4 was to identify a comprehensive outcome set and develop preliminary banks of items of mobility among individuals with ABI, using NLP.Results of all Manuscripts will generate scientific evidence of useful knowledge related to standardizing terms and labels for mobility (common language) that will inform the creation of a Core Outcome Set and develop the ontology for mobility. The ontology of mobility will help reduce heterogeneity in terms related to mobility, making it easier for researchers, clinicians, and patients to identify a Core Outcome Set of mobility domains important to measure in clinical care and research
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 | 0,006 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,015 | 0,009 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».