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Record W4390669254 · doi:10.1002/hipo.23598

Impaired perceptual discrimination of complex objects in older adults at risk for dementia

2024· article· en· W4390669254 on OpenAlexafffundabout
Lydia Jiang, Jessica Robin, Nathanael Shing, Negar Mazloum‐Farzaghi, Natalia Ladyka‐Wojcik, Niroja Balakumar, Nicole D. Anderson, Jennifer D. Ryan, Morgan D. Barense, Rosanna K. Olsen

Bibliographic record

VenueHippocampus · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsBaycrest HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchAlzheimer Society
KeywordsPsychologyPerirhinal cortexCognitionDementiaTemporal lobePerceptionAudiologyRecognition memoryCognitive declineAlzheimer's diseaseDiseaseNeuroscienceMedicinePathology

Abstract

fetched live from OpenAlex

Tau pathology accumulates in the perirhinal cortex (PRC) of the medial temporal lobe (MTL) during the earliest stages of the Alzheimer's disease (AD), appearing decades before clinical diagnosis. Here, we leveraged perceptual discrimination tasks that target PRC function to detect subtle cognitive impairment even in nominally healthy older adults. Older adults who did not have a clinical diagnosis or subjective memory complaints were categorized into "at-risk" (score <26; n = 15) and "healthy" (score ≥26; n = 23) groups based on their performance on the Montreal Cognitive Assessment. The task included two conditions known to recruit the PRC: faces and complex objects (greebles). A scene condition, known to recruit the hippocampus, and a size control condition that does not rely on the MTL were also included. Individuals in the at-risk group were less accurate than those in the healthy group for discriminating greebles. Performance on either the face or size control condition did not predict group status above and beyond that of the greeble condition. Visual discrimination tasks that are sensitive to PRC function may detect early cognitive decline associated with AD.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.404
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.314
Teacher spread0.293 · 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.

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

Citations5
Published2024
Admission routes3
Has abstractyes

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