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Record W7111511513

Review of the Efficacy of Methods in the Early Detection of Vascular Dementia

2022· article· W7111511513 on OpenAlexaboutno aff

Bibliographic record

VenueNSUWorks (Nova Southeastern University) · 2022
Typearticle
Language
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaVascular dementiaNeurocognitiveCognitionNeuropsychologyCognitive declineWechsler Adult Intelligence ScaleWechsler Memory ScaleTest (biology)
DOInot available

Abstract

fetched live from OpenAlex

Various assessments are used to detect cognitive decline throughout the stages of vascular dementia (VaD); however, the literature lacks a review of assessment efficacy. Early signs of VaD may include a decline in executive functioning, processing speed, attention, visuospatial abilities, and memory retrieval deficits. This review examines peer-reviewed publications addressing the validity and efficacy of widely accepted (and some lesser-known) neuropsychological assessments used to detect cognitive dysfunction in VaD. in addition to some common related medical assessments. Articles reviewed here include selected case-control studies and a case. The Cambridge Cognitive Examination (CAMCOG) and the Montreal Cognitive Assessment (MoCA) surpassed the Mini-Mental Status Examination (MMSE) in the efficacy of early diagnosis. The MMSE and Mattis Dementia Rating Scale (DRS) had good power in diagnostic differentiation. The Clinical Dementia Rating (CDR) effectively detected the early transitional stage prior to the onset of clinical dementia. At the same time, Addenbrooke's Cognitive Examination-III (ACE-III) and the Wechsler Memory Scales (WMS-III) exhibited high diagnostic accuracy and differentiation power, indicating strong efficacy in early detection. Tests like the Clock Drawing Test (CDT), Olfactory Function Test (OFT), and Pocket Smell Test (PST) are valuable complements to other assessments. Results indicated support for the use of multiple assessments to increase diagnostic confidence. Effective methods to detect VaD early may assist in early treatment intervention, and additional exploration of this topic is indicated. Objective: The objective of this review was to compare methods of assessing for the early detection of vascular neurocognitive disorder (VaD) to understand what assessments aid clinicians in forming the most accurate diagnosis. Data Selection: Peer-reviewed studies conducted between 1998 and 2020 were abstracted from the EBSCO and the ScienceDirect database. Search criteria was confined to vascular dementia, early detection/cognitive decline, and neuropsychological assessment efficacy. Data on late-stage/non-vascular dementia, medical conditions, and mental disorders were excluded. A final selection of fourteen articles were reviewed based on criteria that excluded information on outdated assessments, assessments integrated with other tests, and any references to data obtained prior to DSM-III-R. An additional six articles on commonly used medical assessments were reviewed. Data Synthesis: Synthesis of findings revealed the Cambridge Cognitive Examination (CAMCOG) and Montreal Cognitive Assessment (MoCA) surpassed the efficacy of the Mini Mental Status examination (MMSE) in early diagnosis. The MMSE and Mattis Dementia Rating Scale (DRS) exhibited good diagnostic differentiation power. The Clinical Dementia Rating (CDR) effectively detected early features of a transitional stage prior to the onset of full-blown dementia. There were findings for the usefulness of the Addenbrooke’s Cognitive Examination-III (ACE-III) and Wechsler Memory Scale-3rd Edition (WMS-III). The Clock Drawing Test (CDT), Olfactory Function Test (OFT), and Pocket Smell Test (PST) can supplement other assessments for early VaD. Conclusions: Support was found for administering more than one assessment to increase confidence in diagnosis. Each test had its own strengths and weaknesses such that using only one test results in less accurate diagnosis.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.036
GPT teacher head0.321
Teacher spread0.285 · 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.

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
Published2022
Admission routes1
Has abstractyes

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