MétaCan
Menu
Back to cohort

Spatiotemporal Coupling of the Tongue in Amyotrophic Lateral Sclerosis

2012· article· en· W2165303302 on OpenAlexafffund
Mili Kuruvilla-Dugdale, Jordan R. Green, Yana Yunusova, Kathy Hanford

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2012
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsSunnybrook HospitalUniversity of Toronto
FundersNational Institute on Deafness and Other Communication DisordersALS Society of CanadaUniversity of Wisconsin-Madison
KeywordsAmyotrophic lateral sclerosisNeuroscienceTonguePsychologyMedicineAudiologyDiseasePathology

Abstract

fetched live from OpenAlex

PURPOSE: The primary aim of the investigation was to identify deficits in spatiotemporal coupling between tongue regions in amyotrophic lateral sclerosis (ALS). The relations between disease-related changes in tongue movement patterns and speech intelligibility were also determined. Methods The authors recorded word productions from 11 individuals with ALS with mild, moderate, and severe dysarthria using an x-ray microbeam during word productions. A coupling index based on sliding window covariance was used to determine disease-related changes in the coupling between the tongue regions across each word. RESULTS: The results indicated decreased spatiotemporal coupling of mid-posterior tongue regions and reduced tongue speed in the ALS-moderate subgroup. Changes in the range of tongue coupling relations and speed of movement were highly correlated with speech intelligibility. CONCLUSIONS: These results provide new insights into the loss of lingual motor control due to ALS and suggest that measures of tongue performance may provide useful indicators of bulbar disease severity and progression.

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)0.002
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.184
GPT teacher head0.483
Teacher spread0.299 · 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

Citations48
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Speech Language and Hearing ResearchSame topicDysphagia Assessment and ManagementFrench-language works237,207