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Record W2811026151 · doi:10.1044/2018_jslhr-l-17-0304

Evaluation of Linguistic Markers of Word-Finding Difficulty and Cognition in Parkinson's Disease

2018· article· en· W2811026151 on OpenAlexaboutno aff
Kara M. Smith, Sharon Ash, Sharon X. Xie, Murray Grossman

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2018
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institute on AgingNational Institutes of Health
KeywordsCognitionPsychologyDementiaMontreal Cognitive AssessmentVerbal fluency testMultivariate analysisMultivariate statisticsAudiologyDiseaseCognitive impairmentMedicineNeuropsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Purpose: Early cognitive symptoms such as word-finding difficulty (WFD) in daily conversation are common in Parkinson's disease (PD), but studies have been limited by a lack of feasible, quantitative measures. Linguistic analysis, focused on pauses in speech, may yield markers of impairment of cognition and communication in PD. The objective of this study was to evaluate the relationship of linguistic markers in semistructured speech to WFD symptoms and cognitive function in PD. Method: Speech recordings of description of the Cookie Theft picture in 53 patients with PD without dementia and 23 elderly controls were analyzed with Praat software. Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005), category naming fluency, and confrontation naming tests were administered. Questionnaires rating WFD symptoms and cognitive instrumental activities of daily living were completed. We determined the relationships between (a) pause length and location, (b) MoCA score, and (c) WFD symptoms, using Pearson's correlations and multivariate regression models. Results: Compared with controls, patients with PD had more pauses within utterances as well as fewer words per minute and a lower percentage of well-formed sentences. Pauses within utterances differed significantly between PD-mild cognitive impairment and normal cognition (p < .001). Words per minute and percentage of well-formed sentences were predictive of MoCA in multivariate regression models. Pauses before verbs were associated with patient-reported severity of WFD symptoms (p = .006). Conclusions: Linguistic markers including pauses within utterances distinguish patients with PD with mild cognitive symptoms from elderly controls. These markers are associated with global cognitive function before the onset of dementia. Pauses before verbs and grammatical markers may index early cognitive symptoms such as WFD that may interfere with functional communication. Supplemental Material: https://doi.org/10.23641/asha.6615401.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.105
GPT teacher head0.429
Teacher spread0.324 · 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

Citations32
Published2018
Admission routes1
Has abstractyes

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