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How Parkinson's Disease Affects Non‐verbal Communication and Language Processing

2008· article· en· W2070509597 on OpenAlexafffund
Marc D. Pell, Laura Monetta

Bibliographic record

VenueLanguage and Linguistics Compass · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsAffect (linguistics)ComprehensionCognitionPsychologyCognitive psychologyNonverbal communicationLiteral (mathematical logic)Tone (literature)LinguisticsCommunicationNeuroscience

Abstract

fetched live from OpenAlex

Abstract In addition to difficulties that affect movement, many adults with Parkinson's disease (PD) experience changes that negatively impact on receptive aspects of their communication. For example, some PD patients have difficulties processing non‐verbal expressions (facial expressions, voice tone) and many are less sensitive to ‘non‐literal’ or pragmatic meanings of language, at least under certain conditions. This chapter outlines how PD can affect the comprehension of language and non‐verbal expressions and considers how these changes are related to concurrent alterations in cognition (e.g., executive functions, working memory) and motor signs associated with the disease. Our summary underscores that the progressive course of PD can interrupt a number of functional systems that support cognition and receptive language, and in different ways, leading to both primary and secondary impairments of the systems that support linguistic and non‐verbal communication.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.268
Teacher spread0.247 · 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

Citations41
Published2008
Admission routes2
Has abstractyes

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