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Record W2891909682 · doi:10.1002/mds.110

Detecting Mild Cognitive Deficits in <scp>P</scp>arkinson's <scp>D</scp>isease: <scp>C</scp>omparison of <scp>N</scp>europsychological <scp>T</scp>ests

2018· review· en· W2891909682 on OpenAlexaff
Jeroen Hoogland, Lennard L. van Wanrooij, Judith A. Boel, Jennifer G. Goldman, Glenn T. Stebbins, John C. Dalrymple‐Alford, Connie Marras, Charles H. Adler, Carme Junqué, Kenn Freddy Pedersen, Brit Mollenhauer, Cyrus P. Zabetian, Paul J. Eslinger, Simon J.G. Lewis, Ruey‐Meei Wu, Martin Klein, María Rodríguez‐Oroz, Davide Maria Cammisuli, Paolo Barone, Roberta Biundo, Rob M.A. de Bie, Ben Schmand, Alexander I. Tröster, David J. Burn, Irene Litvan, J. Vincent Filoteo, Gert J. Geurtsen, Daniel Weintraub

Bibliographic record

VenueMovement Disorders · 2018
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsToronto Western HospitalUniversity of Toronto
FundersNational Institute of Neurological Disorders and Stroke
KeywordsNeuropsychologyCognitionPsychologyNeuropsychological testEffects of sleep deprivation on cognitive performanceCognitive testWorking memoryNeuropsychological assessmentParkinson's diseaseExecutive functionsClinical psychologyAudiologyDiseaseMedicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Numerous neuropsychological tests and test versions are used in Parkinson's disease research, but their relative capacity to detect mild cognitive deficits and their comparability across studies are unknown. The objective of this study was to identify neuropsychological tests that consistently detect cognitive decline in PD across studies. METHODS: Data from 30 normed neuropsychological tests across 20 international studies in up to 2908 nondemented PD patients were analyzed. A subset of 17 tests was administered to up to 1247 healthy controls. A 2-step meta-analytic approach using standardized scores compared performance in PD with normative data. RESULTS: Pooled estimates of the differences between PD and site-specific healthy controls identified significant cognitive deficits in PD patients on 14 test scores across 5 commonly assessed cognitive domains (attention or working memory, executive, language, memory, and visuospatial abilities), but healthy control performance was statistically above average on 7 of these tests. Analyses based on published norms only, as opposed to direct assessment of healthy controls, showed high between-study variability that could not be accounted for and led to inconclusive results. CONCLUSIONS: Normed neuropsychological tests across multiple cognitive domains consistently detect cognitive deficits in PD when compared with site-specific healthy control performance, but relative PD performance was significantly affected by the inclusion and type of healthy controls versus the use of published norms only. Additional research is needed to identify a cognitive battery that can be administered in multisite international studies and that is sensitive to cognitive decline, responsive to therapeutic interventions, and superior to individual cognitive tests. © 2018 International Parkinson and Movement Disorder Society.

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)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.044
GPT teacher head0.325
Teacher spread0.281 · 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 designNot applicable
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

Citations70
Published2018
Admission routes1
Has abstractyes

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