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Record W2530125405 · doi:10.17116/jnevro20161166246-53

Clinical and neuropsychological features of Alzheimer’s disease in the combination with cerebrovascular disease

2016· article· en· W2530125405 on OpenAlexaboutno aff
N A Trusova, О С Левин, A. V. Arablinsky

Bibliographic record

VenueS S Korsakov Journal of Neurology and Psychiatry · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaMedicineNeuropsychologyVascular dementiaNeuroimagingMontreal Cognitive AssessmentDiseaseNeuropsychological assessmentPsychiatryDysexecutive syndromeCognitionPhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

AIM: To study clinical/neuropsychological and neuroimaging characteristics of Alzheimer's disease in the combination with cerebrovascular disease (CVD). MATERIAL AND METHODS: Ninety patients with dementia, including 35 patients with AD, 35 patients with mixed dementia (MD) and 20 patients with vascular dementia, were examined. The character of dementia was established according to NINCDS-ADRDA and NINDS-AIREN criteria. The neuropsychological battery included Addenbrooke's Cognitive Examination (ACE-R), Montreal Cognitive Assessment scale (MoCA), fluency test and the visual memory test (SCT). Affective and behavioral disorders were assessed with the Cornell Depression Scale in patients with dementia and a short version of NPI-4 in AD patients. Focal and diffuse changes were assessed with MRI. RESULTS AND CONCLUSION: Patients with MD were older, had more often pseudobulbar syndrome (74%), postural instability (66%), frontal gait disorders (57%), Neuropsychological profile of patients with MD had mixed amnestic-dysexecutive character and, depending on the severity of vascular pathology, was closer to AD or to vascular dementia. Neuroimaging changes of patients with MD were correlated with clinical manifestations. The authors propose the approaches to the differential diagnosis of MD that allow to determine the main directions of treatment more precisely and to predict disease course.

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 score0.023
Threshold uncertainty score0.268

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.027
GPT teacher head0.303
Teacher spread0.276 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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