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Record W3007522514 · doi:10.21037/aes.2019.ab029

AB029. Investigating the link between reduced reading ability and cognitive decline in older adults with acquired vision impairment: a feasibility study

2019· article· en· W3007522514 on OpenAlexaffabout
Swaathi Balaji, Karine Elalouf, Andrea Urqueta-Alfaro, Julie-Andrée Marinier, Aaron Johnson, Natalie A. Phillips, Eva Kehayia, M. Kathleen Pichora‐Fuller, Walter Wittich

Bibliographic record

VenueAnnals of Eye Science · 2019
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversity of TorontoMcGill UniversityAssociation for Canadian StudiesMcGill University Health CentreCentre for Interdisciplinary Research in RehabilitationConcordia UniversityUniversité de Montréal
Fundersnot available
KeywordsAudiologyCognitionPsychologyReading (process)Hearing lossAssociation (psychology)Test (biology)FluencyComprehensionNeuropsychologyCognitive skillDevelopmental psychologyMedicinePsychiatryComputer science

Abstract

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Background: Reading enables us to obtain information, to engage in leisure, educational, business and other activities, and fosters cognitive stimulation. Acquired vision loss can have a negative effect on an individual’s ability to read, and, by extension, reduce overall quality of life. In addition, a growing body of research on the association between reading and cognition has linked a decrease in reading ability with negative changes in cognitive function. Therefore, the purpose of the current study is to test the feasibility of a research protocol that will be used to study the association between reduced reading ability and cognitive impairment, specifically in older adults with acquired vision loss. Methods: Seven participants (age range, 27–60 years, Mage =44, 4 males) completed a series of questionnaires and assessments that measured their cognitive, hearing, and reading abilities. All participants had normal hearing, vision and cognition, with the exception of one (two pre-existing hearing conditions). Cognition was tested using the Rey Auditory Verbal Learning Test (RAVLT), the RAVLT 50-word Recognition test, the Verbal Fluency Test, an auditory Trail-Making task, and an auditory 1-N-back memory task. Otoscopy was used to assess ear health, and hearing was measured with self-administered computerized audiometry (Home Hearing Test) and a speech-in-noise test (Canadian Digit Triplet Test). Questionnaires included the Hearing Handicap Inventory for the Elderly, and a multiple-choice questionnaire on subjective reading ability and reading habits. Finally, reading acuity, speed and comprehension were assessed using the International Reading Speed Texts and the MNRead. Results: In testing the feasibility of this protocol, the uninterrupted length of testing time, including obtaining consent, was determined to be between 60–90 min. Several of the assessments and tests (RAVLT, MNRead, audiogram) were reported to be mentally taxing. However, all pilot participants were able to complete all tasks. With the exception of standardized tests, some of the instructions required refinement and clarification, in order to better explain the tasks for each test. Conclusions: Anticipating that the protocol will be more demanding for the ultimate target population, who will be older adults with sensory impairment, these pilot results were used to guide a strategy for collecting the dependent measures. It was decided to administer the most important measure in each of the domains (e.g., MNRead, audiogram, RAVLT) to ensure that data on vision, hearing, reading and cognition are collected. Depending on the level of fatigue and motivation of the participants, secondary measures in each domain (the International Reading Speed Texts, CDTT, the 1-N-back task) will be administered afterward. Multiple sessions and breaks will be offered as needed. Data collection with novice vision rehabilitation clients will begin at the end of January 2019.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.038
GPT teacher head0.387
Teacher spread0.349 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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