MétaCan
Menu
Back to cohort
Record W1983486513 · doi:10.1111/jnp.12018

Source memory in normal aging and Parkinson's disease

2013· article· en· W1983486513 on OpenAlexafffund
Patrick S. R. Davidson, Shaun P. Cook, Leslie McGhan, Thomas P. Bouchard, Richard Camicioli

Bibliographic record

VenueJournal of Neuropsychology · 2013
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of AlbertaAlberta Hospital EdmontonBruyèreUniversity of Ottawa
FundersCanadian Institutes of Health ResearchSanten
KeywordsPsychologyParkinson's diseaseCognitive psychologyDiseaseMedicineInternal medicine

Abstract

fetched live from OpenAlex

Several theorists have described memory in Parkinson's disease (PD) as involving an amplification of the deficits seen in normal aging, and drawn parallels between PD and frontal lesion patients. Both normal aging and frontal lobe damage impair memory for the context in which one has encountered information (i.e., source memory). We thus sought to determine whether PD patients would show especially poor source memory. We assessed memory for perceptual (voice), spatial (location of loudspeaker), and temporal (list) source memory in 18 PD patients, 23 healthy older adults, and 35 young people. Although both the healthy aged and PD groups performed more poorly than the young on most of the memory tests, the PD patients failed to show significantly greater impairments than the healthy older adults. The PD patients did perform more poorly, however, on a measure of executive function (the Wisconsin Card Sorting Test [WCST]). We discuss potential reasons why PD had a surprisingly minimal effect on source memory in our study, and relate our data to broader theories of memory impairment in Parkinson's disease.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.047
GPT teacher head0.303
Teacher spread0.256 · 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

Citations11
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueJournal of NeuropsychologySame topicMemory and Neural MechanismsFrench-language works237,207