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Record W2947971241 · doi:10.1016/j.heares.2019.05.011

Animal and human studies on developmental monaural hearing loss

2019· review· en· W2947971241 on OpenAlexafffund
Karen A. Gordon, Andrej Kral

Bibliographic record

VenueHearing Research · 2019
Typereview
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersArmy Research OfficeCanadian Institutes of Health ResearchDeutsche Forschungsgemeinschaft
KeywordsMonauralAudiologyBinaural recordingSound localizationPsychologyAuditory systemHearing lossMedicine

Abstract

fetched live from OpenAlex

Asymmetric hearing has been the focus of many studies on brain plasticity in the past. Recently, the topic has gained clinical importance in cases with sequential cochlear implantation or in cases with deafness in one ear and preserved hearing in the other ear. Convergent evidence from animal experiments and from hearing impaired children suggest that asymmetric hearing during early development can reorganize the central auditory representation of the two ears with the consequence of a "stronger" representation of the better hearing ear with a "weaker" representation of the other, more poorly hearing, ear. These changes lead to a persistent aural preference for one ear, demonstrated by asymmetric speech comprehension when each ear is tested separately. Further, binaural integration is compromised as shown by reduced binaural fusion and reduced sensitivity for binaural cues. Finally, the data demonstrate a significant difference in cortical plasticity in response to juvenile monocular deprivation in the visual system and juvenile monaural deafness in the auditory system. The topic represents a unique example of translational research whereby mechanisms explored in animal models are combined with data from children to understand the clinical consequences of asymmetric hearing in early development.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.641
GPT teacher head0.556
Teacher spread0.085 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations52
Published2019
Admission routes2
Has abstractyes

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