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Record W2766758157 · doi:10.1016/j.jalz.2017.06.1650

[P3–432]: A VISUAL COGNITIVE ASSESSMENT TOOL FOR THE DIAGNOSIS OF YOUNG‐ONSET DEMENTIA: A BIOMARKER‐SUPPORTED STUDY

2017· article· en· W2766758157 on OpenAlexaboutno aff
Levinia Lim, Jayne Yi Tan, Eveline Franco da Silva, Shahul Hameed, Simon Ting, Adeline Su Lyn Ng, Nagaendran Kandiah

Bibliographic record

VenueAlzheimer s & Dementia · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaMontreal Cognitive AssessmentBiomarkerMemory clinicCognitionMedicineCognitive impairmentInternal medicineRecallCognitive testPsychologyAudiologyOncologyPediatricsPsychiatryDiseaseCognitive psychology

Abstract

fetched live from OpenAlex

Background: Recent work has demonstrated the Montreal Cognitive Assessment (MoCA) and Visual Cognitive Assessment Test (VCAT) 1to be superior to MMSE in detecting cognitive impairment.However, the diagnostic performance of these cognitive screening tools is not well known in patients with young onset dementia (YOD) of the Alzheimer's type.Methods: Patients under the age of 65-year-old with memory complaints were recruited from the National Neuroscience Institute in a prospective study.Demographics, cognitive data and biomarker data including cerebrospinal fluid (CSF) were collected.Cognitive tests were administered by trained psychologists and CSF Amyloidb, total tau and phosphor-tau were performed using ELISA techniques.Sensitivity (Se), Specificity (Sp), Positive Predicting Value (PPV), Negative Predicting Value (NPV) were calculated for MMSE, MoCA and VCAT using a 2x2 table where patients were classified by their CSF results based on the Tau/Amyloidb Duit's score 2 (CSF negative¼ 0.52, CSF positive¼ >0.52,) and whether they were cognitively impaired based on a validated cut-off on the respective screening tools.VCAT individual domain scores were also correlated with domain-specified tests like the WMS story recall, REYS copy task, ADAS cancellation task, Boston Naming and Colour Trials 2. Results: A total of 29 Young Dementia patients with CSF Amyloidb and tau were recruited, and 16 of them fulfilled Duit's criteria for AD.Their mean (SD) age was 57.07 (6.65), mean (SD) year of education was 11.62 (4.18).44.8% were males and 75.9% were Chinese.MMSE attained the lowest Se of 68.75% while MoCA and VCATwere quite comparable on Se and PPVat 93.75% vs 87.50 % and 85.71% vs 84,62% respectively, VCAT performed the most favorably in Sp (84.62%) and PPV (87.50%).VCAT individual domains of memory (r¼0.836,p<0.001), visuo-spatial (r¼0.746,p<0.001), language (r¼0.732,p<0.001) and executive function (r¼0.793,p<0.001) showed significant correlation to the other domain-specific tests.Conclusions: Our results support that VCAT will be a useful and effective tool in the diagnosis of Young Onset dementia.The high performance of the VCAT in this cohort is likely related to the large range of cognitive domains tested, including visual perception and agnosia. 1 Kandiah N, et al.Early detection of dementia in multilingual populations: Visual Cognitive Assessment Test (VCAT).

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)0.002
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.060
GPT teacher head0.411
Teacher spread0.350 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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