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Enregistrement W3017905551 · doi:10.1111/jgs.16514

Examining Older Adult Cognitive Status in the Time of <scp>COVID</scp> ‐19

2020· letter· en· W3017905551 sur OpenAlexaboutno aff
Nathan Hantke, Christine E. Gould

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2020
Typeletter
Langueen
DomainePsychology
ThématiqueAging and Gerontology Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)GerontologyCognitionBetacoronavirusPandemicVirologyPsychiatryDiseaseInternal medicineInfectious disease (medical specialty)Outbreak

Résumé

récupéré en direct d'OpenAlex

To the Editor: The rapid onset of the coronavirus disease 2019 (COVID-19) pandemic has left many providers ill equipped to continue to provide care as usual. As older adults are particularly at risk for mortality with COVID-19, most providers have rightly pivoted to clinical care via telephone and virtual video visits. Recent research suggests older adults are open to the idea of virtual visits, often preferring them as compared to face-to-face appointments for specialty mental health and dementia care. However, not all clinical services are easily translated into a virtual environment (eg, cognitive assessment), resulting in providers either utilizing creativity or foregoing clinical tools during the health crisis. This letter briefly reviews the current state of remote cognitive assessment, with the goal of outlining appropriate clinical measures for older adults. The present most popular methods of cognitive assessment often do not lend themselves well to virtual visits, as they require hands-on manipulation of stimuli or carefully standardized administration of visual material. The process of creating psychometrically sound tests or translating a test across modalities is unfortunately a cumbersome process. Several studies have examined intraclass correlation coefficients (ICCs) between virtual and face-to-face visits for select neuropsychological measures, suggesting these measurements are reliable across modalities1, 2 and show good criterion validity.3 But, teleneuropsychological research has primarily focused on providing services to rural patients via video teleconference from a primary medical center to a rural clinic. Providing services directly to a patient's home introduces multiple latent variables that may be detrimental to construct validity, and makes extrapolating extant research to home-based virtual visits complicated. Adequate internet connection speeds, camera quality, privacy, and access to a distraction-free environment may contribute to variability in assessment when conducted to the home rather than from clinic to clinic. While teleneuropsychological assessment is increasingly showing clinical potential, providers may wish to be mindful of its strengths, limitations, and appropriate uses for brief cognitive assessment. Traditional cognitive screeners have also shown promise for translation into a video modality. Measures, such as the Mini-Mental State Examination (MMSE) and common mental health questionnaires such as the Geriatric Depression Scale, appear to be diagnostically comparable to in-person clinical visits.4 Mildly modified administration of the Montreal Cognitive Assessment (MoCA) has shown high ICC,5 and there is an audio-visual version of the MoCA now available online, modified for telehealth administration (Table 1). Versions of the MoCA for older adults with hearing and vision impairment are in development as well.6 Beyond cognitive measures modified for video-based administration, telephone-based cognitive assessment has a rich research history and is more likely to be designed initially for the telephone modality, as compared to translated from in-person normative data. The Telephone Interview for Cognitive Status is appropriate for older adults, aged 60 to 98 years, takes approximately 10 minutes, and shows strong correlation with the MMSE.7 Another measure, the Cognitive Telephone Screening Instrument, contains six subtests assessing multiple cognitive domains and shows good convergent validity with the MMSE.8 There is also a modified version of the MoCA available that is appropriate for telephone use. The Brief Test of Adult Cognition provides a comparatively more extensive assessment, taking 20 minutes and showing good construct and concurrent validity with traditional neuropsychological measures, but is presently only available for research purposes.9 This measure also prompts the assessor to conduct a brief test regarding hearing by repeating a series of five numbers before beginning, which could be adapted for any telephone-based measure. Despite the limitations of providing healthcare during the COVID-19 pandemic, providers of older adult care have several options for assessing cognitive status to supplement a clinical interview. Extant measures range from modified traditional screeners to neuropsychological batteries assessing multiple cognitive domains, albeit in a limited fashion. The sudden transition to an entirely telemedicine healthcare system was jarring for most providers, and it appears likely the COVID-19 pandemic will permanently alter healthcare in some capacity. At present, the remote assessment of cognition primarily consists of traditional measures "shoe horned" into a video modality for screening purposes, and not likely to replace more extensive in-person assessment. Yet, the healthcare professionals privileged with providing care to older adults may increasingly be called on to provide telemedicine-based services in the future. Increased competence in technology-mediated healthcare and the construction of telehealth-based cognitive measures will likely become imperative moving forward. Future research designing cognitive measures that utilize and embrace the strengths of telehealth will become vital within the changing landscape of our healthcare systems. Views expressed in this article are those of the authors and not necessarily those for the Department of Veterans Affairs or the federal government. Dr Gould received research support from Meru Health, Inc, for an investigator-initiated trial. Dr Hantke reported no financial relationship with commercial interests. Both authors denied personal conflicts of interest. Dr Hantke and Dr Gould both significantly contributed to this submission. Authors denied any sponsor role in the design, methods, subject recruitment, data collections, analysis, and preparation of this letter.

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)0,003
score de la tête « metaresearch » (Gemma)0,034
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,017

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,034
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0020,001
Intégrité de la recherche0,0060,005
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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.

Tête enseignante Opus0,041
Tête enseignante GPT0,338
Écart entre enseignants0,297 · 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'étudeObservationnel
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations38
Publié2020
Routes d'admission1
Résumé présentoui

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