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Record W2018825896 · doi:10.1002/mds.23690

Tongue control for swallowing in Parkinson's disease: Effects of age, rate, and stimulus consistency

2011· article· en· W2018825896 on OpenAlexafffund
Pascal van Lieshout, Catriona M. Steele, Anthony E. Lang

Bibliographic record

VenueMovement Disorders · 2011
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalToronto Western HospitalToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsParkinson's diseaseSwallowingTongueMedicineDiseaseMovement disordersPhysical medicine and rehabilitationDysphagiaStimulus (psychology)AudiologyPsychologySurgeryPathologyCognitive psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with Parkinson's disease often suffer from swallowing problems, especially at more advanced stages of the disease. Efficient swallows require well-coordinated tongue movements during bolus flow, but little is known about such movements in Parkinson's disease. METHODS: The current study presents data on tongue movements for patients with mild to moderate Parkinson's disease (n=10), age-matched adults (n=13), and younger healthy adults (n=15). RESULTS: Participants with Parkinson's disease showed smaller and more variable movements in the horizontal movement plane, indicating that tongue movements are affected in early stages of Parkinson's disease. CONCLUSIONS: The small and more variable movements in the horizontal plane of Patients with Parkinson's disease may pose challenges for swallowing liquids efficiently and safely.

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)0.008
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
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)0.0020.000

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 head0.023
GPT teacher head0.327
Teacher spread0.304 · 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

Citations35
Published2011
Admission routes2
Has abstractyes

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