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Record W1769058546

Clinical approach to monitoring variability associated with adductor spasmodic dysphonia.

2011· article· en· W1769058546 on OpenAlexaff
Jeffrey C. Yeung, Kevin Fung, Catherine C Bornbaum, Adam M. B. Day, Agnieszka Dzioba, Vijay Parsa, Tyler Phillips Levee, Philip C. Doyle

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsAudiologySentencePerceptionPsychologyCorrelationRating scaleMetric (unit)MedicineDevelopmental psychologyComputer scienceMathematicsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: Adductor spasmodic dysphonia (ADSD) is a voice disorder characterized by considerable intra- and intersubject variability. Although objective, acoustic measures of voice may provide a metric for ADSD, such measures can be inefficient in documenting such characteristics. This project integrated a simple auditory-perceptual measure termed "laryngeal overpressure" (LO) with measures of acoustic variability. METHODS: Ten adults diagnosed with ADSD were sequentially followed over a period of 3 to 6 months. Standard voice recordings were obtained at each point, and acoustic measures were gathered. Additionally, three experienced listeners then rated LO using a visual analogue scale, and acoustic variability was assessed relative to the measure of LO. RESULTS: Listener ratings of LO did not differ across the three-sentence stimuli and were highly correlated (r = .828 and .909 for naive and experienced listeners, respectively). A strong correlation was identified between the acoustic measure of harmonics to noise ratio and the all-voiced sentence stimuli (r = .710). CONCLUSION: LO appears to provide an easy clinical method of documenting voice change over time in those with ADSD. Although additional methods of voice monitoring may be used, the use of LO may provide the opportunity for a standard and reliable approach to the clinical monitoring of voice variability in those presenting with ADSD.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.064
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.104
GPT teacher head0.281
Teacher spread0.177 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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