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Record W3002791081 · doi:10.1097/aud.0000000000000824

“Aural Patching” After Bilateral Cochlear Implantation Is Challenging for Children With Prior Long-Term Unilateral Implant Experience

2020· article· en· W3002791081 on OpenAlexaff
Parvaneh Abbasalipour, Blake C. Papsin, Karen A. Gordon

Bibliographic record

VenueEar and Hearing · 2020
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of TorontoHospital for Sick ChildrenPontifical Institute of Mediaeval Studies
Fundersnot available
KeywordsAudiologyCochlear implantMedicineCochlear implantationImplantSpeech perceptionPerceptionPsychologySurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the use of "aural patching" as a strategy to potentially reduce the known persistence of aural preference in children receiving bilateral cochlear implants (CIs) with long inter-implant delays by removing the first device to increase stimulation to the second implanted side. DESIGN: Children/adolescents who received a second CI at 12.8 ± 3.5 years of age after 9.4 ± 2.9 years of unilateral CI use were asked to remove their first CI for regular periods daily (aural patching). Their compliance was monitored, and asymmetries in speech perception were measured at the end of the study period. RESULTS: Partial adherence to aural patching over the first few months of bilateral hearing use markedly declined with time. As expected, the group demonstrated asymmetries in speech perception that were not significantly affected by the limited aural patching. CONCLUSIONS: The aural patching protocol was a challenge to maintain for most children and families studied, reflecting both the expected aural preference for the first implanted ear and their challenges to reverse it.

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 score0.227
Threshold uncertainty score0.424

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 head0.286
Teacher spread0.252 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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