ArticleHNO2024
Evaluative audiometry after cochlear implant provision.
Article in HNO, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 10 citations in OpenAlex.
- Trial
- Article
- Machine Learning Versus Simple Clinical Models for Cochlear Implant Outcome Prediction.Audiology research · 2025Article
- Optimizing CI Systems for Better Recognition of Soft Speech -the Concept of Broad-Range Mapping.Laryngoscope investigative otolaryngology · 2025Article
- External Validation and Extension of a Cochlear Implant Performance Prediction Model: Analysis of the Oldenburg Cohort.Audiology research · 2025Article
- [Fitting of cochlear implant systems].HNO · 2025Review
- Factors to Describe the Outcome Characteristics of a CI Recipient.Journal of clinical medicine · 2024Article
- Outcome Prediction of Speech Perception in Quiet and in Noise for Cochlear Implant Candidates Based on Pre-Operative Measures.Journal of clinical medicine · 2024Article
- Evolving a Model for Cochlear Implant Outcome.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOne of the main treatment goals in cochlear implant (CI) patients is to improve speech perception. One of the target parameters is speech intelligibility in quiet. However, treatment results show a high variability, which has not been sufficiently explained so far. The aim of this noninterventional retrospective study was to elucidate this variability using a selected population of patients in whom etiology was not expected to have a negative impact on postoperative speech intelligibility. MATERIALS AND
methodsAudiometric findings of the CI follow-up of 28 adult patients after 6 months of CI experience were evaluated. These were related to the preoperative audiometric examination and evaluated with respect to a recently published predictive model for the postoperative monosyllabic score.
resultsInclusion of postoperative categorical loudness scaling and hearing loss for Freiburg numbers in the model explained 55% of the variability in fitting outcomes with respect to monosyllabic word recognition.
conclusionThe results of this study suggest that much of the cause of variability in fitting outcomes can be captured by systematic postoperative audiometric checks. Immediate conclusions for CI system fitting adjustments may be drawn from these results. However, the extent to which these are accepted by individual patients and thus lead to an improvement in outcome must be subject of further studies, preferably prospective.
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Registered trials
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.