Evidence map›Paper›PMID 39415539›Full record

ArticleCochlear implants international2024

Effects of automatic auditory scene classification on speech perception in noise and real-world functional communication in children using cochlear implants.

Teresa Y C Ching, Vicky Zhang, Esti Nel, Sanna Hou, Paola Incerti, Anke Plasmans

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In one paragraph

Article in Cochlear implants international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Teresa Y C ChingMacquarie School of Education, Macquarie University, Sydney, Australia.ORCID 0000-0002-1588-5599
Vicky ZhangNational Acoustic Laboratories, Sydney, Australia.
Esti NelCochlear Ltd., Sydney, Australia.
Sanna HouNational Acoustic Laboratories, Sydney, Australia.
Paola IncertiNational Acoustic Laboratories, Sydney, Australia.
Anke PlasmansCochlear Technology Centre, Mechelen, Belgium.

Funding

Longitudinal outcomes of hearing-impaired children: early vs later interventionR01DC008080 · NIDCD · AUSTRALIAN HEARING SERVICES · PI EASWAR, VIJAYALAKSHMI · 2007 to 2023
$4.6M
NIDCD NIH HHS R01 DC008080
6 · The paper itself

Abstract

objectivesTo investigate the effects of automatic scene classification (SCAN) on speech perception in noise and real-world functional performance in children using cochlear implants (CIs).

methodsWe used a within-subjects repeated measures design in two studies. The first study assessed speech perception in noise with or without SCAN enabled in 25 school-aged children. The second study evaluated functional auditory performance in real life. Parents of 18 children provided ratings using the Parents' Evaluation of Aural/oral Performance of Children (PEACH) questionnaire; and children provided ratings using the Self Evaluation of Listening Function (SELF) questionnaire. Analyses of variance with repeated measures were used to examine the effect of SCAN.

resultsOn average, speech perception in noise was significantly better with SCAN enabled (mean SRT: -4.1 dB; SD: 4.0), compared to SCAN disabled (mean SRT: 0.5 dB; SD: 3.5). Children's functional performance in real life was similar between the two device settings.

conclusionAutomatic auditory scene classification provides significant benefits for speech perception in noise (4.6 dB improvement). On average, there were no perceived detrimental or beneficial effects in real life. These findings support the use of SCAN in CIs for young children.

Indexed as

Cochlear ImplantationCochlear ImplantsDeafnessNoiseSpeech PerceptionAdolescentChildFemaleHumansMaleSurveys and QuestionnairesAutomatic auditory scene classificationChildrenCochlear implantFunctional performance in real lifeSCANSpeech perception in spatially separated noise

Identifiers

PMID39415539
PMCPMC11952983

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Registered trials

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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.