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Record W2059122872 · doi:10.1017/s1355617707070361

Differential impact of age on verbal memory and executive functioning in chronic kidney disease

2007· article· en· W2059122872 on OpenAlexaff
Wendy Loken Thornton, R. Jean Shapiro, Sirad Deria, Shannon R. Gelb, A. T. Hill

Bibliographic record

VenueJournal of the International Neuropsychological Society · 2007
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsExecutive functionsDiseasePsychologyVerbal memoryCognitive psychologyMedicineClinical psychologyCognitionDevelopmental psychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

We compared aspects of verbal memory and executive functioning in 51 community-dwelling persons with chronic kidney disease (CKD) and 55 healthy controls matched on age and education. Depressive symptoms were assessed with the Centre for Epidemiological Studies-Depression Scale (CES-D), and illness variables included glomerular filtration rate (GFR) and hemoglobin. Findings indicate that persons with CKD exhibited poorer performance on measures of memory (CVLT-II) and executive functioning (DKEFS Trailmaking Test B and Color-Word Interference Tests) in comparison with healthy controls. Furthermore, performance decrements were magnified in older CKD participants on measures of verbal memory and inhibition. Nearly half of CKD participants aged 61 and older exhibited significant impairments in verbal memory and inhibition in comparison to matched controls. Cognitive performance in CKD was not associated with measures of illness severity. The differences observed were not accounted for by depressive symptoms, which were only weakly associated with cognitive performance, and negatively associated with age. Findings highlight the need for further exploration of the etiologies and functional consequences of the neuropsychological presentation of CKD.

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 score0.054
Threshold uncertainty score0.188

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 head0.320
Teacher spread0.300 · 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

Citations55
Published2007
Admission routes1
Has abstractyes

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