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Record W2143911129 · doi:10.3109/21678421.2013.817585

Bulbar and speech motor assessment in ALS: Challenges and future directions

2013· article· en· W2143911129 on OpenAlexaff
Jordan R. Green, Yana Yunusova, Mili Kuruvilla-Dugdale, Jun Wang, Gary L. Pattee, Lori Synhorst, Lorne Zinman, James D. Berry

Bibliographic record

VenueAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration · 2013
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsAmyotrophic lateral sclerosisSwallowingMotor neurone diseasePhysical medicine and rehabilitationAudiologyPsychologyMotor functionMedicineDiseasePathologySurgery

Abstract

fetched live from OpenAlex

Bulbar motor deterioration due to amyotrophic lateral sclerosis (ALS) leads to the eventual impairment of speech and swallowing functions. Despite these devastating consequences, no standardized diagnostic procedure for assessing bulbar dysfunction in ALS exists and adequate objective markers of bulbar deterioration have not been identified. In this paper, we consider objective measures of speech motor function, which show promise for forming the basis of a comprehensive, quantitative bulbar motor assessment in ALS. These measures are based on the assessment of four speech subsystems: respiratory, phonatory, articulatory, and resonatory. The goal of this research is to design a non-invasive, comprehensive bulbar motor assessment instrument intended for early detection, monitoring of disease progression, and clinical trial application. Preliminary data from an ongoing study of bulbar motor decline are presented, which demonstrate the potential clinical efficacy of the speech subsystem approach.

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)0.099
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.099
Threshold uncertainty score0.523

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0990.040
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0040.003
Science and technology studies0.0010.005
Scholarly communication0.0050.012
Open science0.0060.005
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.250
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations220
Published2013
Admission routes1
Has abstractyes

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