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Record W3017905551 · doi:10.1111/jgs.16514

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

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

Bibliographic record

VenueJournal of the American Geriatrics Society · 2020
Typeletter
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)GerontologyCognitionBetacoronavirusPandemicVirologyPsychiatryDiseaseInternal medicineInfectious disease (medical specialty)Outbreak

Abstract

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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 patientʼs 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.476
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.338
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations38
Published2020
Admission routes1
Has abstractyes

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