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Record W4362601157 · doi:10.1097/aud.0000000000001354

Asymmetric Hearing Loss in Adult Cochlear Implant Recipients: Results and Recommendations From a Multisite Prospective Clinical Trial

2023· article· en· W4362601157 on OpenAlexfundno aff
Jill B. Firszt, Laura K. Holden, Noel Dwyer, Ruth M. Reeder, Michael Strube

Bibliographic record

VenueEar and Hearing · 2023
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
FundersNational Institute on Deafness and Other Communication DisordersNational Institutes of HealthYork University
KeywordsMedicineHearing lossAudiologyCochlear implantHearing aidCandidacySpeech perceptionImplantAudiometryOtorhinolaryngologySurgeryPsychologyPerception

Abstract

fetched live from OpenAlex

OBJECTIVE: A multisite clinical trial was conducted to obtain cochlear implant (CI) efficacy data in adults with asymmetric hearing loss (AHL) and establish an evidence-based framework for clinical decision-making regarding CI candidacy, counseling, and assessment tools. Study hypotheses were threefold: (1) 6-month postimplant performance in the poor ear (PE) with a CI will be significantly better than preimplant performance with a hearing aid (HA), (2) 6-month postimplant performance with a CI and HA (bimodal) will be significantly better than preimplant performance with bilateral HAs (Bil HAs), and (3) 6-month postimplant bimodal performance will be significantly better than aided, better ear (BE) performance. DESIGN: Forty adults with AHL from four, metropolitan CI centers participated. Hearing criteria for the ear to be implanted included (1) pure-tone average (PTA, 0.5, 1, 2 kHz) of >70 dB HL, (2) aided, monosyllabic word score of ≤30%, (3) duration of severe-to-profound hearing loss of ≥6 months, and (4) onset of hearing loss ≥6 years of age. Hearing criteria for the BE included (1) PTA (0.5, 1, 2, 4 kHz) of 40 to 70 dB HL, (2) currently using a HA, (3) aided, word score of >40%, and (4) stable hearing for the previous 1-year period. Speech perception and localization measures, in quiet and in noise, were administered preimplant and at 3-, 6-, 9-, and 12-months postimplant. Preimplant testing was performed in three listening conditions, PE HA, BE HA, and Bil HAs. Postimplant testing was performed in three conditions, CI, BE HA, and bimodal. Outcome factors included age at implantation and length of deafness (LOD) in the PE. RESULTS: A hierarchical nonlinear analysis predicted significant improvement in the PE by 3 months postimplant versus preimplant for audibility and speech perception with a plateau in performance at approximately 6 months. The model predicted significant improvement in postimplant, bimodal outcomes versus preimplant outcomes (Bil HAs) for all speech perception measures by 3 months. Both age and LOD were predicted to moderate some CI and bimodal outcomes. In contrast with speech perception, localization in quiet and noise was not predicted to improve by 6 months when comparing Bil HAs (preimplant) to bimodal (postimplant) outcomes. However, when participants' preimplant everyday listening condition (BE HA or Bil HAs) was compared with bimodal performance, the model predicted significant improvement by 3 months for localization in quiet and noise. Lastly, BE HA results were stable over time; a generalized linear model analysis revealed bimodal performance was significantly better than performance with a BE HA at all postimplant intervals for most speech perception measures and localization. CONCLUSIONS: Results revealed significant CI and bimodal benefit for AHL participants by 3-months postimplant, with a plateau in CI and bimodal performance at approximately 6-months postimplant. Results can be used to inform AHL CI candidates and to monitor postimplant performance. On the basis of this and other AHL research, clinicians should consider a CI for individuals with AHL if the PE has a PTA (0.5, 1, 2 kHz) >70 dB HL and a Consonant-Vowel Nucleus-Consonant word score ≤40%. LOD >10 years should not be a contraindication.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.161
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.097
GPT teacher head0.389
Teacher spread0.292 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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