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Record W2018876595 · doi:10.1159/000058330

Neuropsychological Deficit in Early Subcortical Vascular Dementia: Comparison to Alzheimer’s Disease

2002· article· en· W2018876595 on OpenAlexaff
Latchezar Traykov, Sophie Baudic, Marie-Claude Thibaudet, Anne‐Sophie Rigaud, Alain Smagghe, François Boller

Bibliographic record

VenueDementia and Geriatric Cognitive Disorders · 2002
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsHôpital Notre-Dame
Fundersnot available
KeywordsDementiaVascular dementiaNeuropsychologyPsychologyDegenerative diseaseAlzheimer's diseaseDiseaseCentral nervous system diseaseNeuroscienceCognitive disorderPsychiatryMedicineCognitionPathology

Abstract

fetched live from OpenAlex

To further clarify the cognitive syndrome in subcortical vascular dementia (VaD), we investigated 20 patients with early-stage VaD as compared with 30 patients with Alzheimer's disease (AD) and 22 normal controls using episodic memory, attention/executive function and language tests. The patient groups were closely matched in terms of age, education and severity of dementia. The VaD patients had a significantly better free recall, cued recall and recognition memory than AD patients, the recognition being within normal limits in VaD. In addition, VaD patients had a greater number of perseverative errors during the Modified Card Sorting test, while AD patients exhibited more perseverations of semantic fluency. The results of retrieval deficit syndrome and increased number of perseverations during tasks sensitive to frontal lobe function are in agreement with the studies emphasizing the importance of frontal dysfunction in subcortical VaD. These findings are relevant for the early diagnosis of VaD and might be useful in the differential diagnosis 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 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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.035
GPT teacher head0.281
Teacher spread0.246 · 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

Citations89
Published2002
Admission routes1
Has abstractyes

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