ArticleAudiology & neuro-otology2022
Using Clinical Audiologic Measures to Determine Cochlear Implant Candidacy.
Article in Audiology & neuro-otology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Access and Attrition Across Audiology and Cochlear Implant Care Among Older Adults with Bilateral Sensorineural Hearing Loss.Otology & neurotology open · 2026Article
- Access to Cochlear Implantation: Trends in Surgeon Volume and Training.The Laryngoscope · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
introductionOnly a small percentage (6-10%) of patients who are candidates receive cochlear implants (CIs). One potential reason contributing to low usage rates may be confusion regarding which patients to refer for CI evaluation. The extent to which information provided by standard clinical audiologic assessments is sufficient for selecting appropriate CI evaluation referrals is uncertain. The objective of this study is to evaluate the capacity of standard clinical audiologic measures to differentiate CI candidates from noncandidates.
methodThe study design is a retrospective review of a prospectively maintained CI database from a university-based tertiary medical center of 518 patients undergoing CI evaluations from 2012 to 2020. Each ear of each patient was treated as an independent value. Receiver operating characteristic (ROCs) curves were constructed using aided AzBio sentence recognition scores in quiet and aided AzBio +10 dB signal-to-noise ratio scores <60% as binary classifiers for CI candidacy. For each ROC, we examined the capacity of multiple pure-tone thresholds, pure-tone average (PTA), and CNC word recognition scores (WRSs) measured under earphones to determine CI candidacy. Area under the curve ROC (AUC-ROC) values were calculated to demonstrate the capacity of each model to differentiate CI candidates from noncandidates.
resultsVariables with the greatest capacity to accurately differentiate CI candidates from noncandidates using aided AzBio in quiet scores were earphone CNC WRS, earphone pure-tone threshold at 1,000 Hz, and earphone PTA (AUC-ROC values = 0.86-0.88). Using aided AzBio +10 scores as the measure for candidacy, only CNC word recognition had a fair capacity to identify candidates (AUC-ROC value = 0.73). Based on the ROCs, a 1,000 Hz pure-tone threshold >50 dB HL, PTA >57 dB HL, and a monosyllabic WRS <60% can each serve as individual indicators for referral for CI evaluations.
conclusionThe current study provides initial indicators for referral and a first step at developing evidence-based criteria for CI evaluation referral using standard audiologic assessments.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.