Psychometric Evaluation and Workflow Integration Study of a Tablet-Based Tool to Detect Mild Cognitive Impairment in Older Adults: Protocol for a Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: With the rapid aging of the global population, experts anticipate a surge in the prevalence of mild cognitive impairment (MCI) and dementia worldwide. It is argued that developing more sensitive, easy to administer, and valid MCI screening tools for use in primary care settings may initiate timely clinical and personal care planning and treatment, enabling early access to programs and services. Including functional competence measures in screening tests makes them more ecologically valid and may help to identify cognitive deficits at an earlier stage. OBJECTIVE: We aim to conduct a preliminary evaluative study comparing the sensitivity, specificity, and reliability of the BrainFx Screen (referred to as SCREEN hereafter), a novel digital tool designed to assess functional competence and detect early signs of cognitive impairment, with the Quick Mild Cognitive Impairment, a validated and highly sensitive tool that detects MCI in the older adult population. We will also investigate the perceived usefulness and integration of the SCREEN into primary care practice to identify demonstrable impacts on clinical workflow and health care providers' (HCP) perceptions of its success as a screening tool. Patients' perceptions of completing the SCREEN and its impact on their quality of life will also be explored. METHODS: This study has a concurrent, mixed methods, prospective, and quasi-experimental design. Participants will be recruited from 5 primary care family health teams (FHTs; defined by multidisciplinary practice and capitated funding) across southwestern Ontario, Canada. Participants will include HCPs, patients, care partners, and FHT administrative executives. Patients 55 years and older with no history of diagnoses for MCI, dementia, or Alzheimer disease rostered in one of the FHTs participating in the study will be eligible to participate. Their care partners will help triangulate the qualitative data collected from patients. Participating FHTs will identify an occupational therapist from their site to participate in the study; this HCP will both administer the research protocol and participate in semistructured in-depth interviews and questionnaires. Principal component analysis will be conducted on the SCREEN data to understand the test components better. Tests comparing sensitivity, specificity, and test-retest reliability will assess the validity of SCREEN as a screening tool for MCI. RESULTS: This paper describes the study protocol and its activities to date. Data collection was halted early because of COVID-19 restrictions on research activity, and data analysis is currently in progress. CONCLUSIONS: At the end of the project, we anticipate having an initial comparative evaluation of the SCREEN as a tool for early detection of MCI in primary care older adult patient populations. Resource constraints on this research study limit our ability to conduct a randomized controlled trial; however, the results will assist developers of the SCREEN in determining whether rigorous controlled testing is warranted. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25520.
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 | 0,052 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,006 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,004 |
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 ».