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Record W2060512647 · doi:10.1080/13825580490904219

Word List Generation Performancein Alzheimer's Disease and Vascular Dementia

2006· article· en· W2060512647 on OpenAlexaff
Quintin E. Poore, Lisa J. Rapport, Darren R. Fuerst, Pamela Keenan

Bibliographic record

VenueAging Neuropsychology and Cognition · 2006
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsSunnybrook Hospital
Fundersnot available
KeywordsCategorical variableDementiaVascular dementiaWord (group theory)DiseasePsychologyAudiologyMedicinePathologyLinguisticsStatisticsMathematics

Abstract

fetched live from OpenAlex

Word list generation (WLG) was examined among clinical samples of individuals with Alzheimer's disease (AD) (n = 73) or ischemic vascular dementia (IVD) (n = 85), equivalent in age, education, current and estimated premorbid intellectual functioning, and proportion of men and women. The AD group performed significantly better than did the IVD group on lexical WLG, and a trend was observed indicating superior performance among the IVD group on categorical WLG. Within-groups, comparisons of group means, and profile analyses of individual performance patterns all indicated that persons with AD demonstrated a lexical > categorical pattern significantly more often than did IVD participants. The absolute difference in average performance between the AD and IVD groups on lexical and categorical WLG was small; the findings, however, generally support the clinical utility of the lexical > categorical WLG pattern in the differential diagnosis of AD and IVD. The patterns of performance support the presence of relative impairment in semantic processing among the individuals with AD and global deficits in retrieval and processing speed in individuals with IVD.

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 score0.168
Threshold uncertainty score0.415

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 head0.020
GPT teacher head0.293
Teacher spread0.273 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

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