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Record W4220657800 · doi:10.1080/13825585.2022.2055739

The effects of simulated and actual visual impairment on the Montreal Cognitive Assessment

2022· article· en· W4220657800 on OpenAlexaffabout
Zoey Stark, Elliot Morrice, Caitlin Murphy, Walter Wittich, Aaron Johnson

Bibliographic record

VenueAging Neuropsychology and Cognition · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsCentre Intégré de Santé et de Services Sociaux des LaurentidesUniversité de MontréalSanté MontérégieCentre intégré de santé et de services sociaux de la Montérégie-CentreCentre intégré de santé et de services sociaux de Chaudière-AppalachesCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre de réadaptation Lethbridge-Layton-MackayConcordia University
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitive impairmentCognitionPsychologyCognitive psychologyAudiologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Many cognitive assessments include a visual component; however, adults may experience a decline in visual acuity with age. Scores on cognitive assessments of adults with visual impairments are typically lower than adults with normal vision, however, it is unclear if these lower scores are a consequence of cognitive or visual impairment. We measured the impact of simulated visual impairment on a cognitive screening measure. Undergraduate students were administered the Montreal Cognitive Assessment (MoCA) under three vision conditions (20/20, simulated 20/80, simulated 20/200). We found a main effect of vision condition on test performance such that there is a statistically significant difference between scores on the 20/20 and 20/80 conditions and 20/200. However, no differences were observed between 20/80 and 20/200. Participants' performance decreased with simulated impairments. A secondary between-subject analysis was conducted on a sample of older adults with and without vision impairment; no differences were found.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.389
Teacher spread0.371 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes2
Has abstractyes

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