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Record W4387439288 · doi:10.1093/arclin/acad067.080

A - 63 Neuropsychiatric and Cognitive Correlates of Pareidolias in Dementia with Lewy Bodies

2023· article· en· W4387439288 on OpenAlexaboutno aff
Madeleine P Smith, Jiangxia Wang, James B. Leverenz, Nathan H. Heller, Susan H Magsaman, Arnold B. Bakker, Cyrus B Zabetian, Odinachi Oguh, Carol F. Lippa, Oscar L. López, Sarah Berman, David J. Irwin, Douglas Galasko, Irene Litvan, Jori Fleisher, James E. Galvin, Andrea Bozoki, Marwan N. Sabbagh, Dylan Wint, Brenna Cholerton, Alexander Pantelyat, Vidyulata Kamath

Bibliographic record

VenueArchives of Clinical Neuropsychology · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDementia with Lewy bodiesDementiaCognitionPsychologyNeuroscienceMedicineDiseasePathology

Abstract

fetched live from OpenAlex

Abstract Objective Pareidolias represent visual illusions of meaningful objects in ambiguous stimuli. Prior research has demonstrated phenomenological similarities between pareidolias and visual hallucinations (VH) and the potential clinical utility of pareidolias in discriminating dementia with Lewy bodies (DLB) from Alzheimer’s dementia (ad). Though pareidolias have been linked with VH severity in DLB, the relationships between pareidolias and other neuropsychiatric symptoms have not been explored in large DLB samples. Method Individuals meeting 2017 McKeith criteria for probable DLB (n = 114) were enrolled in a multi-site systematic longitudinal study of the US DLB Consortium. Study assessments included an informant-rated psychiatric inventory (NPI), Montreal Cognitive Assessment (MoCA), and Noise Pareidolia Task (NPT). Ninety-five participants completed all clinical assessments. To examine baseline relationships between pareidolias responses (NPT) and the presence of 12 neuropsychiatric symptom dimensions (NPI), we used Poisson regression models with robust standard error estimates adjusted for age, education, disease duration, cognition (MoCA), and cholinesterase inhibitor use. Results Contrary to expectation, the presence of VH was not associated with pareidolias in DLB (Incidence Rate Ratio = 1.37, p = 0.275). The presence of both delusions (IRR = 2.11, p = 0.01) and depression (IRR = 1.79, p = 0.015) was associated with greater pareidolias. Additionally, overall cognitive dysfunction was associated with pareidolias (IRR = 0.90, p < 0.001). Conclusions Our findings indicate that pareidolias are associated with poorer cognition and neuropsychiatric dimensions beyond VH, including depression and delusions. As pareidolias have been put forth as a surrogate marker of VH in DLB, future studies examining pareidolias in conjunction with comprehensive psychiatric/cognitive assessment and neuroimaging will further elucidate the clinical utility of pareidolia testing.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.407
Teacher spread0.360 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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