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Record W2903836717 · doi:10.1002/mdc3.12714

Effect of Levodopa on Speech Dysfluency in Parkinson's Disease

2018· article· en· W2903836717 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMovement Disorders Clinical Practice · 2018
Typearticle
Languageen
FieldPsychology
TopicStuttering Research and Treatment
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsStutteringLevodopaAudiologyPsychologyParkinson's diseaseFluencyDiseaseMedicineInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT Objective To examine the effect of levodopa medication on speech dysfluency in Parkinson's disease. Methods Fifty‐one individuals with Parkinson's disease (IWPD) read aloud during off‐ and on‐ medication states. Total speech dysfluencies were calculated from transcriptions of recorded speech samples. Results Severity of speech dysfluency was not significantly related to the severity of motor symptoms, duration of disease, levodopa equivalent dosage, or age. When the IWPD were divided into two groups based on dysfluency severity, there was a significant group‐by‐medication state interaction. There was a significant correlation between the medication‐related change in speech dysfluency and the off ‐medication severity of speech dysfluency measure (r = −0.46). Conclusions The results of this study indicate that levodopa medication can have a significant effect on speech dysfluency. The beneficial levodopa effect appears to be related to the severity of the off‐ medication speech dysfluency. Results did not provide strong support for the excess dopamine theory of stuttering in IWPD. A dualistic model of the effects of dopamine on speech fluency in PD is proposed.

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.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
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)0.0010.001

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 head0.038
GPT teacher head0.462
Teacher spread0.424 · 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