Digital Innovations for Clinical Assessment in Acquired Brain Injury: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Acquired brain injury (ABI) is a leading global cause of morbidity, affecting millions, many of whom face a diverse range of cognitive, physical, and psychological challenges, often made worse due to limited access to timely assessment and appropriate care. In recent years, digital technologies have emerged as potential tools to support more accessible, efficient, and scalable methods for assessment; however, the breadth of research in this area remains unclear. OBJECTIVE: This scoping review aimed to identify and synthesize contemporary research on how digital technologies may help screen, assess, and monitor the complications of ABI in order to uncover trends, themes, and priorities for future research. METHODS: Following the Arksey and O'Malley framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, a systematic search was conducted across Embase, MEDLINE, and Scopus, as well as four clinical trial registries to help capture gray literature. A search string incorporating terms related to "ABI," "clinical assessment," and "digital tools" was developed a priori. Studies from 2013 to 2024 leveraging digital health tools, for example, smartphones, tablets, websites, telemedicine, and virtual reality, to aid ABI complication assessment were included. Exclusion criteria comprised studies involving bespoke clinical hardware (eg, radiographs and EEG monitors), nonhuman subjects, or review articles. Following this, data synthesis and domain mapping were performed. RESULTS: Of 5293 screened records, 88 met the inclusion criteria: 2 retrospective studies, 4 qualitative studies, 35 cohort studies, 42 cross-sectional studies, and 5 randomized controlled trials. The median sample included 26 participants with ABI; 51 studies also involved non-ABI participants (median of 10 participants included). Digital platforms varied, with 45 studies using smartphone or tablet technologies, 23 PC or web-based platforms, 11 telemedicine solutions, and 9 virtual reality platforms. The predominant research themes included the use of digital technology to aid screening for traumatic brain injury, identifying or monitoring symptoms or functional outcomes; physical examination, the assessment of cognition and communication, and providing a comprehensive consultation. Most tools were reported to be well-tolerated, with accuracy often described as comparable to standard assessments. However, the studies were heterogeneous, with limited validation of tools across broad representative populations or multiple sites or studies. There was also little discussion on potential ethical concerns such as accessibility, access, and data privacy. CONCLUSIONS: This investigation provides an extensive overview of current research trends and highlights the need for larger, more rigorous studies to optimize the use of digital technologies in ABI assessment, as well as gaps in the assessment of common complications. Expanding research into underexplored ABI complications, broadening the scope of assessments to include a broader range of complications, and including larger, more diverse populations will be critical for advancing the field and improving outcomes for individuals with ABI.
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 | 0,027 | 0,129 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,032 | 0,029 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».