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Record W2074620505 · doi:10.1186/s13195-014-0071-4

Differentiating between visual hallucination-free dementia with Lewy bodies and corticobasal syndrome on the basis of neuropsychology and perfusion single-photon emission computed tomography

2014· article· en· W2074620505 on OpenAlexafffund
Michael R Misch, Sara Mitchell, P.L. Francis, Kayla Sherborn, Katayoun Meradje, Alicia A. McNeely, Kie Honjo, Jiali Zhao, Christopher J.M. Scott, Curtis B. Caldwell, Lisa Ehrlich, Prathiba Shammi, Bradley J. MacIntosh, Juan M. Bilbao, Anthony E. Lang, Sandra E. Black, Mario Masellis

Bibliographic record

VenueAlzheimer s Research & Therapy · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health NetworkHealth Sciences CentreSunnybrook Health Science Centre
FundersParkinson Society CanadaUniversity of TorontoCanadian Institutes of Health ResearchTeva Pharmaceutical IndustriesMinistero dello Sviluppo EconomicoSunnybrook FoundationHeart and Stroke Foundation of Canada
KeywordsDementia with Lewy bodiesCorticobasal degenerationLewy bodyPsychologyNeuropsychologyDementiaAudiologyPathologyMedicineNeuroscienceCognitionDisease

Abstract

fetched live from OpenAlex

INTRODUCTION: Dementia with Lewy bodies (DLB) and Corticobasal Syndrome (CBS) are atypical parkinsonian disorders with fronto-subcortical and posterior cognitive dysfunction as common features. While visual hallucinations are a good predictor of Lewy body pathology and are rare in CBS, they are not exhibited in all cases of DLB. Given the clinical overlap between these disorders, neuropsychological and imaging markers may aid in distinguishing these entities. METHODS: Prospectively recruited case-control cohorts of CBS (n =31) and visual hallucination-free DLB (n =30), completed neuropsychological and neuropsychiatric measures as well as brain perfusion single-photon emission computed tomography and structural magnetic resonance imaging (MRI). Perfusion data were available for forty-two controls. Behavioural, perfusion, and cortical volume and thickness measures were compared between the groups to identify features that serve to differentiate them. RESULTS: The Lewy body with no hallucinations group performed more poorly on measures of episodic memory compared to the corticobasal group, including the delayed and cued recall portions of the California Verbal Learning Test (F (1, 42) =23.1, P <0.001 and F (1, 42) =14.0, P =0.001 respectively) and the delayed visual reproduction of the Wechsler Memory Scale-Revised (F (1, 36) =9.7, P =0.004). The Lewy body group also demonstrated reduced perfusion in the left occipital pole compared to the corticobasal group (F (1,57) =7.4, P =0.009). At autopsy, the Lewy body cases all demonstrated mixed dementia with Lewy bodies, Alzheimer's disease and small vessel arteriosclerosis, while the corticobasal cases demonstrated classical corticobasal degeneration in five, dementia with agyrophilic grains + corticobasal degeneration + cerebral amyloid angiopathy in one, Progressive Supranuclear Palsy in two, and Frontotemporal Lobar Degeneration-Ubiquitin/TAR DNA-binding protein 43 proteinopathy in one. MRI measures were not significantly different between the patient groups. CONCLUSIONS: Reduced perfusion in the left occipital region and worse episodic memory performance may help to distinguish between DLB cases who have never manifested with visual hallucinations and CBS at earlier stages of the disease. Development of reliable neuropsychological and imaging markers that improve diagnostic accuracy will become increasingly important as disease modifying therapies become available.

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)0.003
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.069
GPT teacher head0.354
Teacher spread0.284 · 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

Citations20
Published2014
Admission routes2
Has abstractyes

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