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Record W2792623801 · doi:10.1002/mds.27330

Initial cognitive changes in Parkinson's disease

2018· review· en· W2792623801 on OpenAlexaff
Daniel Weintraub, Alexander I. Tröster, Connie Marras, Glenn T. Stebbins

Bibliographic record

VenueMovement Disorders · 2018
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of Toronto
FundersNational Institute of Neurological Disorders and Stroke
KeywordsDementiaCognitionNeuropsychologyParkinson's diseaseDiseaseCognitive impairmentCognitive declinePsychologyMedicineMovement disordersEpidemiologyPsychiatryPathology

Abstract

fetched live from OpenAlex

The focus on cognitive impairment in neurodegenerative diseases, including PD, is shifting from the dementia stage to earlier stages of impairment, including mild cognitive impairment. This shift is driven primarily by the desire to improve long-term outcomes by delivering therapeutic interventions earlier in the clinical course, even presymptomatically in those at highest risk, and at the initial stage in the pathophysiological cascade that underpins common dementia syndromes. This article focuses on key findings and challenges in studying earliest stages of cognitive decline in PD, including a detailed examination of neuropsychological testing, cognitive performance in early and prodromal PD, epidemiological research for PD mild cognitive impairment to date, and expert recommendations for assessment. © 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)0.001
metaresearch head score (Gemma)0.001
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: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.043
GPT teacher head0.343
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 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

Citations59
Published2018
Admission routes1
Has abstractyes

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