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Record W4415750565 · doi:10.1177/08919887251392086

Delusions in Lewy Body Disease: A Cross-Sectional Study on Associated Factors and Lived Experiences

2025· article· en· W4415750565 on OpenAlexaff
Caroline Sirna, Ashay Panse, Panagiotis Alexopoulos, Ella Carol, Orla Keane, Iracema Leroi

Bibliographic record

VenueJournal of Geriatric Psychiatry and Neurology · 2025
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsTrinity College
Fundersnot available
KeywordsLewy bodyDementiaDementia with Lewy bodiesTypologyCognitionDelusionAlzheimer's diseaseRating scale

Abstract

fetched live from OpenAlex

IntroductionDelusions are common in Lewy body disease (LBD), significantly impacting quality of life. This study examined clinical factors, characteristics and themes associated with delusions in LBD.MethodsClinical and demographic factors were compared between 91 individuals attending St. James's Hospital in Ireland with LBD both with and without delusions. Clinical scales include the Clinical Dementia Rating Scale (CDR), Epworth sleepiness scale (ESS), Addenbrooke's Cognitive Evaluation (ACE-III), and Neuropsychiatric Inventory-12 (NPI-12). Themes of delusions extracted from clinical descriptions were mapped onto a typology from primary psychiatric populations.ResultsIndividuals with delusions were older, had higher CDR and ESS scores, lower ACE-III performance, higher scores on the NPI-12, and demonstrated cognitive impairment at the MCI or dementia level. Misidentification delusions were most common, followed by delusions of "being harmed, attacked, or killed" and "residence is not their home".ConclusionThese findings suggest delusions are related to disease stage, sleep, distinct cognitive and neuropsychiatric patterns, and follow a unique thematic typology.

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)0.002
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.319
Teacher spread0.303 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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