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Record W205969823 · doi:10.1159/000336890

Tongue Movements and Their Acoustic Consequences in Amyotrophic Lateral Sclerosis

2012· article· en· W205969823 on OpenAlexafffund
Yana Yunusova, Jordan R. Green, Lauren Greenwood, Jun Wang, Gary L. Pattee, Lorne Zinman

Bibliographic record

VenueFolia Phoniatrica et Logopaedica · 2012
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsSunnybrook Health Science CentreWestern UniversityCentre for Movement DisordersUniversity of Toronto
FundersNational Institute on Deafness and Other Communication DisordersUniversity of Toronto
KeywordsMultiple sclerosisMedicineCommunicationPsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: The relations between acoustic measures and their articulatory bases have rarely been tested in dysarthria but are important for diagnostic and treatment purposes. We tested the association between acoustic measures of F2 range and F2 slope with kinematic measures of tongue movement displacement and speed in individuals with amyotrophic lateral sclerosis (ALS) and healthy controls speaking at normal and slow rates. Relations between acoustic and kinematic measures and speech intelligibility were examined. RESULTS: As healthy controls reduced their speaking rate, their F2 slopes and movement speeds decreased. In talkers with ALS, acoustic and kinematic variables were associated with changes in speaking rate, characteristic of disease progression. Participants with slow rate had shallower F2 slopes and slower movement speeds than those with normal rate. Relations between F2 range and tongue displacement were weaker. F2 slope, displacement, and duration were correlated with speech intelligibility most consistently. CONCLUSION: Findings suggested that F2 slope is a useful marker for tracking disease progression in ALS. F2 slope reflects changes in tongue function with disease progression and is linked to speech intelligibility. Changes in movement speed, however, might be the earliest sign of disease in the tongue.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.051
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.026
GPT teacher head0.268
Teacher spread0.242 · 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.

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

Citations0
Published2012
Admission routes2
Has abstractyes

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