Evidence map›Paper›PMID 37316992›Full record

ReviewEar and hearing

Using Complex Networks in the Hearing Sciences.

Michael S Vitevitch, David B Pisoni, Lauren Soehlke, Tabitha A Foster

Open access · greenAbstract readReview
In one paragraph

Review in Ear and hearing. 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
1.2field-weighted citation impact, top 21% of its field
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, 6 citations in OpenAlex.

  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

4 authors at 2 institutions in 1 country.

Michael S VitevitchUniversity of Kansas, Lawrence, Kansas, USA.
David B PisoniIndiana University, Indianapolis, Indiana, USA.
Lauren SoehlkeUniversity of Kansas, Lawrence, Kansas, USA.
Tabitha A FosterUniversity of Kansas, Lawrence, Kansas, USA.
University of Kansas · USIndiana University – Purdue University Indianapolis · US

Funding

Executive Functioning and Speech-Language Skills in Cochlear Implant UsersR01DC015257 · NIDCD · INDIANA UNIVERSITY INDIANAPOLIS · PI KRONENBERGER, WILLIAM G, PISONI, DAVID B · 2017 to 2021
$3.1M
Multi-Domain Sensory Measures as Biomarkers of Alzheimer's Disease in Preclinical and Prodromal StagesR01AG061788 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI RISACHER, SHANNON L · 2019 to 2023
$3.0M
NIA NIH HHS R01 AG061788NIDCD NIH HHS R01 DC015257
6 · The paper itself

Abstract

In this Point of View, we review a number of recent discoveries from the emerging, interdisciplinary field of Network Science , which uses graph theoretic techniques to understand complex systems. In the network science approach, nodes represent entities in a system, and connections are placed between nodes that are related to each other to form a web-like network . We discuss several studies that demonstrate how the micro-, meso-, and macro-level structure of a network of phonological word-forms influence spoken word recognition in listeners with normal hearing and in listeners with hearing loss. Given the discoveries made possible by this new approach and the influence of several complex network measures on spoken word recognition performance we argue that speech recognition measures-originally developed in the late 1940s and routinely used in clinical audiometry-should be revised to reflect our current understanding of spoken word recognition. We also discuss other ways in which the tools of network science can be used in Speech and Hearing Sciences and Audiology more broadly.

Indexed as

DeafnessHearing LossSpeech PerceptionAudiometry, Pure-ToneHearingHumansSpeech

Identifiers

PMID37316992
PMCPMC10721731
OpenAlexW4380729862

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.