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Record W2081564648 · doi:10.1155/2012/308501

Are Verbal Fluency and Nonliteral Language Comprehension Deficits Related to Depressive Symptoms in Parkinson’s Disease?

2012· article· en· W2081564648 on OpenAlexafffund
Christina Tremblay, Oury Monchi, Carol Hudon, Joël Macoir, Laura Monetta

Bibliographic record

VenueParkinson s Disease · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsComprehensionVerbal fluency testFluencyTask (project management)MedicinePsychologyParkinson's diseaseDiseaseCognitive psychologyAudiologyCognitionPsychiatryNeuropsychologyLinguisticsPathology

Abstract

fetched live from OpenAlex

Depression in Parkinson's disease (PD) is frequently associated with executive deficits, which can influence nonliteral comprehension and lexical access. This study explores whether depressive symptoms in PD modulate verbal fluency and nonliteral language comprehension. Twelve individuals with PD without depressive symptoms, 13 with PD and depressive symptoms (PDDSs), and 13 healthy controls completed a semantic and phonemic verbal fluency task and an indirect speech acts comprehension task. All groups had the same performance in the phonemic fluency task while the PDDS group was impaired in the semantic task. For the indirect speech act comprehension task, no difference was observed between the groups. However, the PDDS group had difficulty answering direct speech act questions. As some language impairments in PD become apparent when depressive symptoms are associated with the disease, it would appear to be important to take the presence of depressive symptoms into account when evaluating language abilities in PD.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.283
Teacher spread0.265 · 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 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

Citations14
Published2012
Admission routes2
Has abstractyes

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