Evidence map›Paper›PMID 30426606›Full record

ArticleInternational journal of language & communication disorders2019

Reconceptualizing developmental language disorder as a spectrum disorder: issues and evidence.

Hope S Lancaster, Stephen Camarata

Open access · bronzeAbstract read
In one paragraph

Article in International journal of language & communication disorders, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
5.4field-weighted citation impact, top 4% 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

30 citing papers in PubMed, 61 citations in OpenAlex.

  1. Direct Instruction Improves Word Learning for Children With Developmental Language Disorder.Journal of speech, language, and hearing research : JSLHR · 2022
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  16. Abilities and Disabilities Among Children With Developmental Language Disorder.Language, speech, and hearing services in schools · 2023
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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

2 authors at 2 institutions in 1 country.

Hope S LancasterDepartment of Speech and Hearing Science, Arizona State University, Tempe, AZ, USA.ORCID 0000-0001-9924-9508
Stephen CamarataDepartment of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN, USA.ORCID 0000-0002-3342-1747
Arizona State University · USVanderbilt University · US

Funding

WRITTEN LANGUAGE OUTCOMES IN CHILDREN WITH LANGUAGE IMPAIRMENTSP50DC002746 · NIDCD · UNIVERSITY OF IOWA · PI TOMBLIN, JAMES BRUCE · 1995 to 2005
$6.0M
Genotype-Phenotype Associations in Reading DisordersF32HD089674 · NICHD · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI LANCASTER, HOPE SPARKS · 2017 to 2019
$189k
National Institute on Deafness and Other Communication Disorders P50 DC002746NICHD NIH HHS F32 HD089674NIDCD NIH HHS P50 DC002746
6 · The paper itself

Abstract

backgroundThere is considerable variability in the presentation of developmental language disorder (DLD). Disagreement amongst professionals about how to characterize and interpret the variability complicates both the research on understanding the nature of DLD and the best clinical framework for diagnosing and treating children with DLD. We describe and statistically examine three primary possible models for characterizing the variability in presentation in DLD: predictable subtypes; individual differences; and continuum/spectrum.

aimsTo test these three models of DLD in a population-based sample using two distinct types of cluster analyses. METHODS & PROCEDURES: This study included children with DLD (n = 505) from the US Epidemiological Study of Language Impairment database. All available language and cognitive measures were included. Two cluster methods were used: Ward's method and K-means. Optimal cluster sizes were selected using Bayesian information criteria (BIC). Bootstrapping and permutation methods were used to evaluate randomness of clustering. OUTCOMES &

resultsBoth clustering analyses yielded more than 10 clusters, and the clusters did not have spatial distinction: many of these clusters were not clinically interpretable. However, tests of random clustering revealed that the cluster solutions obtained did not arise from random aggregation. CONCLUSIONS & IMPLICATIONS: Non-random clustering coupled with a large number of non-interpretable subtypes provides empirical support for the continuum/spectrum and individual differences models. Although there was substantial support for the continuum/spectrum model and weaker support for the individual differences model, additional research testing these models should be completed. Based on these results, clinicians working with children with DLD should focus on creating treatment plans that address the severity of functioning rather than seeking to identify and treat distinct subtypes. Additional consideration should be given to reconceptualizing DLD as a spectrum condition.

Indexed as

Child BehaviorChild LanguageAge FactorsChildCluster AnalysisCognitionDatabases, FactualFemaleHumansLanguage Development DisordersMaleTerminology as Topicdevelopmental language impairmentquantitativespecific language impairment

Identifiers

PMID30426606
PMCPMC6684235
OpenAlexW2901701202

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.