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Record W4408908948 · doi:10.5152/iao.2025.241676

Audio-Vestibular Findings in Young Regular and Non-Regular Personal Music System Users

2025· article· en· W4408908948 on OpenAlexaff
Darshan Devananda, P. Ghosh, Nayana Benny

Bibliographic record

VenueThe Journal of International Advanced Otology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsAutism Canada
Fundersnot available
KeywordsExpression (computer science)AudiologyBiologyMedicineComputer scienceProgramming language

Abstract

fetched live from OpenAlex

Background: Younger adults frequently utilize personal music systems (PMSs) for extended periods for leisure. It has been reported in the literature that hearing abilities are affected in such individuals. However, its effect on auditory processing abilities and the vestibular system remains unclear. Hence, the present study was carried out to investigate the audiological and vestibular functioning in young adults who use PMSs regularly. Methods: Forty participants between 18 and 25 years of age were divided into 2 groups. Group 1 included 20 regular PMSs users from 2 to 3 years, and group 2 comprised 20 participants who were non-regular PMSs users. Detailed audiological evaluations were carried out on 15 participants in each group, and vestibular evaluations were carried out on all the participants. Results: It was observed that the extended high-frequency hearing thresholds and otoacoustic emissions were affected in the regular PMSs users. The gap in noise test and vestibular evoked myogenic potential testing revealed that temporal resolution abilities and vestibular system functioning are also compromised among regular PMS users. Conclusion: Thus, this study highlights the subtle vestibular and auditory impairments that PMS may produce in young adults, as well as the significance of a battery of tests to detect them.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.008
GPT teacher head0.251
Teacher spread0.243 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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