Evidence map›Paper›PMID 38717468›Full record

ArticleJASA express letters2024

Cochlear-implant listeners benefit from training with time-compressed speech, even at advanced ages.

Amara C Ezenwa, Matthew J Goupell, Sandra Gordon-Salant

Abstract read
In one paragraph

Article in JASA express letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

3 authors.

Amara C EzenwaDepartment of Hearing and Speech Sciences, University of Maryland, College Park, Maryland 20742, USAacezenwa@umd.edu, goupell@umd.edu, sgsalant@umd.edu.
Matthew J GoupellDepartment of Hearing and Speech Sciences, University of Maryland, College Park, Maryland 20742, USAacezenwa@umd.edu, goupell@umd.edu, sgsalant@umd.edu.ORCID 0000-0003-2662-3057
Sandra Gordon-SalantDepartment of Hearing and Speech Sciences, University of Maryland, College Park, Maryland 20742, USAacezenwa@umd.edu, goupell@umd.edu, sgsalant@umd.edu.ORCID 0000-0001-8037-9724

Funding

Speech Perception with High Cognitive DemandP01AG055365 · NIA · UNIV OF MARYLAND, COLLEGE PARK · PI GORDON-SALANT, SANDRA M · 2017 to 2021
$8.4M
Peripheral and central contributions to auditory temporal processing deficits and speech understanding in older cochlear implanteesR01DC020316 · NIDCD · UNIV OF MARYLAND, COLLEGE PARK · PI Matthew J. Goupell · 2022 to 2026
$2.7M
Temporal processing and speech understanding in older cochlear implanteesR01AG051603 · NIA · UNIV OF MARYLAND, COLLEGE PARK · PI GOUPELL, MATTHEW J. · 2016 to 2020
$1.9M
NIA NIH HHS P01 AG055365NIA NIH HHS R01 AG051603NIDCD NIH HHS R01 DC020316
6 · The paper itself

Abstract

This study evaluated whether adaptive training with time-compressed speech produces an age-dependent improvement in speech recognition in 14 adult cochlear-implant users. The protocol consisted of a pretest, 5 h of training, and a posttest using time-compressed speech and an adaptive procedure. There were significant improvements in time-compressed speech recognition at the posttest session following training (>5% in the average time-compressed speech recognition threshold) but no effects of age. These results are promising for the use of adaptive training in aural rehabilitation strategies for cochlear-implant users across the adult lifespan and possibly using speech signals, such as time-compressed speech, to train temporal processing.

Indexed as

Cochlear ImplantsSpeech PerceptionAdultAgedAged, 80 and overCochlear ImplantationFemaleHumansMaleMiddle AgedTime Factors

Identifiers

PMID38717468
PMCPMC11075136

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.