Evidence map›Paper›PMID 40670511›Full record

ArticleScientific reports2025

Voice fatigue subtyping through individual modeling of vocal demand reponses.

Mark L Berardi, Susanna Whitling, Eric J Hunter

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mark L BerardiDepartment of Communication Sciences and Disorders, University of Iowa, Iowa City, Iowa, USA. mark-berardi@uiowa.edu.
Susanna WhitlingDepartment of Logopedics Phoniatrics and Audiology, Lund University, Lund, Sweden.
Eric J HunterDepartment of Communication Sciences and Disorders, University of Iowa, Iowa City, Iowa, USA.

Funding

Protecting Teachers’ Voices: Investigating Risk Factors, Conducting Cost AnalysisR01DC012315 · NIDCD · UNIVERSITY OF UTAH · PI HUNTER, ERIC J · 2013 to 2025
$5.5M
NIDCD NIH HHS R01 DC012315NIDCD NIH HHS R01DC012315
6 · The paper itself

Abstract

Recognizing individual variability is essential for developing targeted, personalized medical interventions. Vocal fatigue is a prevalent symptom and complaint among occupational voice users, but its identification has yielded mixed results. Vocal fatigue is a complex issue with heterogeneous biophysiological responses to vocal demands among individuals. This research aims to classify individuals as vocal demand responders to measure changes in vocal performance consistent with state vocal fatigue. A total of 37 participants (19F, 18M) completed a 30-minute vocal loading task (VLT) which consisted of loud speaking with background noise. Participants provided speech samples pre- and post-VLT and rated their vocal effort levels before, every 5 minutes during, and after the VLT. Perceived effort ratings and measured vocal performance from the speech samples were used to classify participants into distinct subgroups of vocal demand responders. Prior to classification there were few detectable changes associated with the VLT. However, the subgroup with both vocal effort and voice production demand responses displayed significant changes consistent with vocal fatigue while the other subgroups did not. These findings support the need for an individual-based approach to subtyping and measuring vocal fatigue and highlight its heterogeneous nature.

Indexed as

VoiceVoice DisordersVoice QualityAdultFemaleHumansMaleMiddle AgedSpeechYoung AdultMachine learningPrecision medicineVocal fatigueVocal loading taskVoice production

Identifiers

PMID40670511
PMCPMC12267405

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.