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Record W4401958586 · doi:10.1038/s41598-024-69620-z

Linear effects of glucose levels on voice fundamental frequency in type 2 diabetes and individuals with normoglycemia

2024· article· en· W4401958586 on OpenAlexaff
Jaycee Kaufman, Jouhyun Jeon, Jessica Oreskovic, Yan Fossat

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsType 2 diabetesEndocrinologyDiabetes mellitusInternal medicineMedicineType 1 diabetes

Abstract

fetched live from OpenAlex

Glucose levels in the body have been hypothesized to affect voice characteristics. One of the primary justifications for voice changes are due to Hooke's law, in which a variation in the tension, mass, or length of the vocal folds, mediated by the body's glucose levels, results in an alteration in their vibrational frequency. To explore this hypothesis, 505 participants were fitted with a continuous glucose monitor (CGM) and instructed to record their voice using a custom mobile application up to six times daily for 2 weeks. Glucose values from CGM were paired to voice recordings to create a sampled dataset that closely resembled the glucose profile of the comprehensive CGM dataset. Glucose levels and fundamental frequency (F0) had a significant positive association within an individual, and a 1 mg/dL increase in CGM recorded glucose corresponded to a 0.02 Hz increase in F0 (CI 0.01-0.03 Hz, P < 0.001). This effect was also observed when the participants were split into non-diabetic, prediabetic, and Type 2 Diabetic classifications (P = 0.03, P = 0.01, & P = 0.01 respectively). Vocal F0 increased with blood glucose levels, but future predictive models of glucose levels based on voice may need to be personalized due to high intraclass correlation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.267
Teacher spread0.256 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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