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Record W2513836911 · doi:10.1155/2016/9291713

Cognitive Training in Parkinson’s Disease: A Review of Studies from 2000 to 2014

2016· review· en· W2513836911 on OpenAlexafffund
Daniel Glizer, Penny A. MacDonald

Bibliographic record

VenueParkinson s Disease · 2016
Typereview
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsParkinson's diseaseMedicineCognitionCognitive trainingDiseaseCognitive declinePsychological interventionInstitutionalisationClinical psychologyDementiaPhysical medicine and rehabilitationPsychiatryPathology

Abstract

fetched live from OpenAlex

Cognitive deficits are prevalent among patients with Parkinson's disease (PD), in both early and late stages of the disease. These deficits are associated with lower quality of life, loss of independence, and institutionalization. To date, there is no effective pharmacological treatment for the range of cognitive impairments presented in PD. Cognitive training (CT) has been explored as an alternative approach to remediating cognition in PD. In this review we present a detailed summary of 13 studies of CT that have been conducted between 2000 and 2014 and a critical examination of the evidence for the effectiveness and applicability of CT in PD. Although the evidence shows that CT leads to short-term, moderate improvements in some cognitive functions, methodological inconsistencies weaken these results. We discuss several key limitations of the literature to date, propose methods of addressing these questions, and outline the future directions that studies of CT in PD should pursue. Studies need to provide more detail about the cognitive profile of participants, include larger sample sizes, be hypothesis driven, and be clearer about the training interventions and the outcome measures.

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)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.927
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.125
GPT teacher head0.403
Teacher spread0.278 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations27
Published2016
Admission routes2
Has abstractyes

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