Evidence map›Paper›PMID 36143210›Full record

ArticleJournal of personalized medicine2022

Application of Signals with Rippled Spectra as a Training Approach for Speech Intelligibility Improvements in Cochlear Implant Users.

Dmitry Nechaev, Marina Goykhburg, Alexander Supin, Vigen Bakhshinyan, George Tavartkiladze

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2022. 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

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.

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

5 authors.

Dmitry NechaevA.N. Severtsov Institute of Ecology and Evolution, 119071 Moscow, Russia.
Marina GoykhburgNational Research Centre for Audiology and Hearing Rehabilitation, 117513 Moscow, Russia.ORCID 0000-0002-2860-0759
Alexander SupinA.N. Severtsov Institute of Ecology and Evolution, 119071 Moscow, Russia.
Vigen BakhshinyanNational Research Centre for Audiology and Hearing Rehabilitation, 117513 Moscow, Russia.
George TavartkiladzeNational Research Centre for Audiology and Hearing Rehabilitation, 117513 Moscow, Russia.ORCID 0000-0003-0118-908X

Funding

Russian Foundation for Basic Research 20-22-00054
6 · The paper itself

Abstract

In cochlear implant (CI) users, the discrimination of sound signals with rippled spectra correlates with speech discrimination. We suggest that rippled-spectrum signals could be a basis for training CI users to improve speech intelligibility. Fifteen CI users participated in the study. Ten of them used the software for training (the experimental group), and five did not (the control group). Software based on the phase reversal discrimination of rippled spectra was used. The experimental group was also tested for speech discrimination using phonetic material based on polysyllabic balanced speech material. An improvement in the discrimination of the rippled spectrum was observed in all CI users from the experimental group. There was no significant improvement in the control group. The result of the speech discrimination test showed that the percentage of recognized words increased after training in nine out of ten CI users. For five CI users who participated in the training program, the data on word recognition were also obtained earlier (at least eight months before training). The increase in the percentage of recognized words was greater after training compared to the period before training. The results allow the suggestion that sound signals with rippled spectra could be used not only for testing rehabilitation results after CI but also for training CI users to discriminate sounds with complex spectra.

Indexed as

cochlear implantshearingrippled spectrumspeech discrimination

Identifiers

PMID36143210
PMCPMC9503413

What OpenQuestion holds

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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.