Evidence map›Paper›PMID 34775699›Full record

ArticleJournal of audiology & otology2022

Efficacy of the Digit-in-Noise Test: A Systematic Review and Meta-Analysis.

Chanbeom Kwak, Jae-Hyun Seo, Yonghee Oh, Woojae Han

Open access · diamondAbstract read
In one paragraph

Article in Journal of audiology & otology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 23% 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

6 citing papers in PubMed, 13 citations in OpenAlex.

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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 3 institutions in 2 countries.

Chanbeom KwakDivision of Speech Pathology and Audiology, College of Natural Sciences, Hallym University, Chuncheon, Korea.
Jae-Hyun SeoDepartment of Otolaryngology-Head & Neck Surgery, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Yonghee OhDepartment of Speech, Language, and Hearing Sciences, University of Florida, Gainesville, FL, USA.
Woojae HanDivision of Speech Pathology and Audiology, College of Natural Sciences, Hallym University, Chuncheon, Korea.
Hallym University · KRCatholic University of Korea · KRUniversity of Florida · US

Funding

National Research Foundation of Korea 2019R1F1A1053060The Ministry of Science and ICT
6 · The paper itself

Abstract

BACKGROUND AND

objectivesAlthough the digit-in-noise (DIN) test is simple and quick, little is known about its key factors. This study explored the considerable components of the DIN test through a systematic review and meta-analysis. MATERIALS AND

methodsAfter six electronic journal databases were screened, 14 studies were selected. For the meta-analysis, standardized mean difference was used to calculate effect sizes and 95% confidence intervals.

resultsThe overall result of the meta-analysis showed an effect size of 2.224. In a subgroup analysis, the patient's hearing status was found to have the highest effect size, meaning that the DIN test was significantly sensitive to screen for hearing loss. In terms of the length of the presenting digits, triple digits had lower speech recognition thresholds (SRTs) than single or pairs of digits. Among the types of background noise, speech-spectrum noise provided lower SRTs than multi-talker babbling. Regarding language variance, the DIN test showed better performance in the patient's native language(s) than in other languages.

conclusionsWhen uniformly developed and well validated, the DIN test can be a universal tool for hearing screening.

Indexed as

AudiometryDigitsHearingScreeningTest in noise

Identifiers

PMID34775699
PMCPMC8755436
OpenAlexW3212330008

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.