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Record W2767922279 · doi:10.1097/aud.0000000000000508

Speech in Noise Perception as a Marker of Cognitive Impairment in HIV Infection

2017· article· en· W2767922279 on OpenAlexaffabout
Yi Zhan, Abigail M. Fellows, Tangkai Qi, Odile Clavier, Sigfrid D. Soli, Xiudong Shi, Jiang Gui, Yuxin Shi, Jay C. Buckey

Bibliographic record

VenueEar and Hearing · 2017
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Deafness and Other Communication DisordersNational Cancer Institute
KeywordsAudiologyMontreal Cognitive AssessmentTympanometryMedicineAudiometryHearing lossSpeech perceptionCognitionPresbycusisPsychologyPerceptionCognitive impairmentPsychiatry

Abstract

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OBJECTIVES: Human immunodeficiency virus positive (HIV+) individuals report hearing difficulties, but standard audiological tests show no, or small, changes in peripheral hearing ability. The hearing complaints may reflect central nervous system (CNS) auditory processing deficits, rather than middle or inner ear problems, and may result from CNS damage due to HIV infection or treatment. If central auditory task performance and cognitive deficits in HIV+ individuals are shown to be related, then central auditory tests might serve as a "window" into CNS function in these patients. DESIGN: We measured cognitive performance (Mandarin Montreal Cognitive Assessment [MoCA]) and speech in noise perception (Mandarin hearing-in-noise test [HINT]) in 166 normal-hearing HIV+ individuals (158 men, 8 women, average age 36 years) at the Shanghai Public Health Clinical Center in Shanghai, China. Data collection included audiometry, tympanometry, and the Amsterdam Inventory of Auditory Handicap (AIAH), which assesses the subjective ability to understand speech and localize sound. RESULTS: Subjects had no middle ear disease and met criteria for normal-hearing sensitivity (all thresholds 20 dB HL or less). A significant negative relationship between speech reception thresholds (SRT) and MoCA scores (r = 0.15, F = 28.2, p < 0.001) existed. Stepwise linear regression showed that when the factors of age, MoCA scores, hearing thresholds, and education level were considered, only age and MoCA scores contributed independently to the SRT results (overall model r = 0.30, F = 38.8, p < 0.001). Subjective hearing complaints from the AIAH supported the HINT results. AIAH and MoCA scores were also related (r = 0.05, F = 8.5, p = 0.004), with those with worse MoCA scores having more problems on the AIAH. When the cohort was divided into those with normal and abnormal performance on the MoCA, those with abnormal performance on the MoCA had significantly higher average SRTs (p < 0.001). CONCLUSIONS: Understanding speech in noise measured both objectively with the HINT and subjectively with the AIAH was inversely related to cognitive abilities despite a normal ability to hear soft sounds determined by audiometry. Although age was also an important independent factor affecting speech perception, the age relationship within the speech findings in this study may represent more than just age-related declines in speech in noise understanding. Although reliable data on disease duration are not available, the older members of this cohort likely had HIV longer and probably had more severe symptoms at presentation than the younger members because early detection and treatment of HIV in Shanghai has improved over time. Therefore, the age relationship may also include elements of disease duration and severity. Speech perception, especially in challenging listening conditions, involves cortical and subcortical centers and is a demanding neurological task. The problems interpreting speech in noise HIV+ individuals have may reflect HIV-related or HIV treatment-related, central nervous damage, suggesting that CNS complications in HIV+ individuals could potentially be diagnosed and monitored using central auditory tests.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.034
GPT teacher head0.324
Teacher spread0.290 · 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

Citations30
Published2017
Admission routes2
Has abstractyes

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