MétaCan
Menu
Back to cohort
Record W2084983866 · doi:10.1055/s-2007-986527

Application of the ICF in Reduced Speech Intelligibility in Dysarthria

2007· article· en· W2084983866 on OpenAlexaff
Allyson D. Dykstra, Mark Hakel, Scott Adams

Bibliographic record

VenueSeminars in Speech and Language · 2007
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsDysarthriaIntelligibility (philosophy)International Classification of Functioning, Disability and HealthPsychologyAudiologyCognitive psychologyMedicineNeuroscienceRehabilitation

Abstract

fetched live from OpenAlex

Regardless of the underlying neuromotor impairment, an almost universal consequence of dysarthria is a reduction in speech intelligibility. The purpose of this article is to examine critically and to discuss issues related directly to speech intelligibility in speakers with dysarthria. Reduced speech intelligibility resulting from dysarthria is examined using the World Health Organization's International Classification of Functioning, Disability and Health (ICF) conceptual framework. We propose that the ICF conceptual framework facilitates an awareness of the multidimensional nature of disablement. Furthermore, the ICF facilitates a broad understanding of the complex nature of dysarthria, ranging from the neuroanatomical and physiological substrates contributing to reduced speech intelligibility, to the effects of this type of communication disorder on an individual's functioning in society and beyond. Finally, a case example is presented that describes how the ICF can be applied to an individual with dysarthria and reduced speech intelligibility.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.005
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.001
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 head0.007
GPT teacher head0.294
Teacher spread0.287 · 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
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

Citations79
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueSeminars in Speech and LanguageSame topicVoice and Speech DisordersFrench-language works237,207