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The Auditory Sensitivity is Increased in Tinnitus Ears

2013· article· en· W2033253604 on OpenAlexaff
Sylvie Hébert, Philippe Fournier, Arnaud Noreña

Bibliographic record

VenueJournal of Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsUniversité de MontréalInternational Laboratory for Brain, Music and Sound Research
FundersAgence Nationale de la Recherche
KeywordsTinnitusHyperacusisAudiologyLoudnessAudiogramHearing lossMedicinePsychology

Abstract

fetched live from OpenAlex

Increased auditory sensitivity, also called hyperacusis, is a pervasive complaint of people with tinnitus. The high prevalence of hyperacusis in tinnitus subjects suggests that both symptoms have a common origin. It has been suggested that they may result from a maladjusted increase of central gain attributable to sensory deafferentation. More specifically, tinnitus and hyperacusis could result from an increase of spontaneous and stimulus-induced activity, respectively. One prediction of this hypothesis is that auditory sensitivity should be increased in tinnitus compared with non-tinnitus subjects. The purpose of this study was to test this prediction by examining the loudness functions in tinnitus ears (n = 124) compared with non-tinnitus human ears (n = 106). Because tinnitus is often accompanied by hearing loss and that hearing loss makes it difficult to disentangle hypersensitivity (hyperacusis) to loudness recruitment, tinnitus and non-tinnitus ears were carefully matched for hearing loss. Our results show that auditory sensitivity is enhanced in tinnitus subjects compared with non-tinnitus subjects, including subjects with normal audiograms. We interpreted these findings as compatible with a maladaptive central gain in tinnitus.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.276
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations152
Published2013
Admission routes1
Has abstractyes

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Same venueJournal of NeuroscienceSame topicHearing, Cochlea, Tinnitus, GeneticsFrench-language works237,207