Evidence map›Paper›PMID 42148440›Full record

ArticleWellcome open research2026

VALIDATION OF A BEHAVIOURAL, SPEECH SPECTRUM/SPEECH BASED mHEALTH HEARING SCREENER FOR CHILDREN.

Deepashree Joshi, Vidya Ramkumar, Ramya Vaidyanath, Anil Prabhakar

Abstract read
In one paragraph

Article in Wellcome open research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Deepashree JoshiFaculty of Audiology and Speech Language Pathology, Sree Balaji Medical College & Hospital, Chennai, Tamil Nadu, 600044, India.
Vidya RamkumarFaculty of Audiology and Speech Language Pathology, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, Tamil Nadu, 600116, India.ORCID https://orcid.org/0000-0002-3590-6721
Ramya VaidyanathFormer Faculty at Department of Audiology, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, Tamil Nadu, 600116, India.
Anil PrabhakarElectrical and Electronic Engineering, Indian Institute of Technology Madras Department of Electrical Engineering, Chennai, Tamil Nadu, 600036, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An affordable mHealth-based subjective hearing screener (SRESHT) for children was developed to support contextualized hearing screening for developing countries. The screener was developed with calibrated commercial headphones and speakers at 60 dB HL with the target goal of identifying children with moderately severe or above degrees of disability (WH0, 2019). The hearing screener includes Behavioral Observation Audiometry (BOA) based screening module (0 to 1 year of age), Speech Spectrum Awareness Task (SSAT) module (1 to 3 years of age), Speech Recognition Task (SRT) module (3 to 6 years of age) and High Risk register (HRR) and Parental Questionnaire (PQ) module (0 to 6 years). The study identified optimal screening modules for detecting hearing loss = 60 dB HL for 0-1 year of age, 1 to 3 years of age and 3 to 6 years of age separately. For infants aged 0-1 year, the combined BOA+HRR module was found to be most suitable (SEN = 86%, SPE = 91%, ACU = 90%). In children aged 1-3 years, SSAT module was found to be suitable (SEN = 98%, SPE = 85%, ACU = 91.2%. For the 3-6 years age group, SRT was found suitable (SEN =95%, SPE = 89%, ACU = 91.43%). When test-based screening methods are not feasible, standalone HRR (0-1 year) and PQ (3-6 years) modules remain viable alternatives, meeting the 80% sensitivity and specificity benchmarks to screen for > = 60dBHL.

Indexed as

Childrencommunity-basedcommunity workersgrassroots workersHearingLow Middle Income Countries Speech awarenessmHealthPediatricScreeningspeech recognitionValidation

Identifiers

PMID42148440
PMCPMC13179490

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.