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Record W2004906905 · doi:10.1037/0894-4105.20.1.123

Cognitive estimation impairment in Alzheimer disease and mild cognitive impairment.

2006· article· en· W2004906905 on OpenAlexaff
Elise J. Levinoff, Natalie A. Phillips, Louis Verret, Lennie Babins, Nora Kelner, Vivian Akerib, Howard Chertkow

Bibliographic record

VenueNeuropsychology · 2006
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsConcordia UniversityMcGill UniversityJewish General HospitalHôpital de l'Enfant-Jésus
Fundersnot available
KeywordsPsychologyCognitionNeuropsychologySemantic memoryWorking memoryAudiologyAlzheimer's diseaseCognitive impairmentEpisodic memoryNeuropsychological testExecutive functionsNeuropsychological assessmentCognitive disorderCognitive testEffects of sleep deprivation on cognitive performanceCognitive psychologyDiseaseNeuroscienceMedicineInternal medicine

Abstract

fetched live from OpenAlex

Intact executive functioning is believed to be required for performance on tasks requiring cognitive estimations. This study used a revised version of a cognitive estimations test (CET) to investigate whether patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) were impaired on the CET compared with normal elderly controls (NECs). Neuropsychological tests were administered to determine the relationship between CET performance and other cognitive domains. AD patients displayed impaired CET performance when compared with NECs but MCI patients did not. Negative correlations between tests of working memory (WM) and semantic memory and the CET were found in NECs and AD patients, indicating that these cognitive domains were important for CET performance. Regression analysis suggests that AD patients were unable to maintain semantic information in WM to perform the task. The authors conclude that AD patients display deficits in working memory, semantic memory, and executive function, which are required for adequate CET performance.

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 score0.024
Threshold uncertainty score0.959

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 studies0.0000.000
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 head0.024
GPT teacher head0.352
Teacher spread0.329 · 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

Citations47
Published2006
Admission routes1
Has abstractyes

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