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Record W1984590366 · doi:10.1159/000095801

Do Lesions Involving the Cortical Cholinergic Pathways Help or Hinder Efficacy of Donepezil in Patients with Alzheimer’s Disease?

2006· article· en· W1984590366 on OpenAlexaff
Toshiya Fukui, Soutaro Hieda, Christian Bocti

Bibliographic record

VenueDementia and Geriatric Cognitive Disorders · 2006
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsDonepezilCholinergicDementiaHyperintensityVascular dementiaPsychologyNeuroscienceAlzheimer's diseaseInternal medicineMedicineDiseaseMagnetic resonance imaging

Abstract

fetched live from OpenAlex

AIMS: To investigate the influences of vascular lesions detected by MRI, lesions involving the cortical cholinergic pathways and hippocampal thickness on therapeutic responsiveness to donepezil in patients with Alzheimer's disease (AD). METHODS: The study cohort contained 67 patients with probable AD. We used the revised Hasegawa Dementia Rating (HDS-R) and the Clock Drawing Test (CDT) to evaluate drug efficacy for 24 months. The Cholinergic Pathways Hyperintensities Scale (CHIPS), a newly developed visual scale, was used to semiquantify lesions on the cholinergic pathways. RESULTS: Over the 24-month period, the results of the CDT showed more apparent and constant association with white matter hyperintensities (WMH) and lesions on the cholinergic pathways than the HDS-R. WMH may enhance, while lesions on the cholinergic pathways may attenuate sensitivity to donepezil treatment when judged by the CDT. No apparent association between the thicknesses of hippocampi with baseline cognition or therapeutic responsiveness to donepezil was found. CONCLUSION: Donepezil may be more efficacious when further executive dysfunction caused by WMH is added to AD dementia and less so when cholinergic reserves are further impinged upon by lesions involving the cortical cholinergic pathways.

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 score0.018
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.269
Teacher spread0.254 · 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

Citations17
Published2006
Admission routes1
Has abstractyes

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