Evidence map›Paper›PMID 38585791›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Lingo: an automated, web-based deep phenotyping platform for language ability.

Lucas G Casten, Tanner Koomar, Muhammad Elsadany, Caleb McKone, Ben Tysseling, Mahesh Sasidharan, J Bruce Tomblin, Jacob J Michaelson

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. 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
0cells of the map it votes in
0citing 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

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

8 authors.

Lucas G CastenInterdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA.ORCID 0000-0001-7690-6405
Tanner KoomarDepartment of Psychiatry, University of Iowa, Iowa City, IA.
Muhammad ElsadanyInterdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA.
Caleb McKoneDepartment of Psychiatry, University of Iowa, Iowa City, IA.
Ben TysselingDepartment of Psychiatry, University of Iowa, Iowa City, IA.
Mahesh SasidharanDepartment of Psychiatry, University of Iowa, Iowa City, IA.
J Bruce TomblinDepartment of Communication Sciences and Disorders, University of Iowa, Iowa City, IA.
Jacob J MichaelsonDepartment of Psychiatry, University of Iowa, Iowa City, IA.ORCID 0000-0001-9713-0992

Funding

University of Iowa Hawkeye Intellectual and Developmental Disabilities Research Center (Hawk-IDDRC)P50HD103556 · NICHD · UNIVERSITY OF IOWA · PI EDWIN TED G. ABEL, Lane Strathearn · 2021 to 2026
$8.5M
Understanding the biology of language impairment through whole genome sequencingR01DC014489 · NIDCD · UNIVERSITY OF IOWA · PI MICHAELSON, JACOB JAMES · 2016 to 2020
$3.1M
Clinical and Translational Science Institute at Childrens National-Mentored Career Development CoreKL2TR001877 · NCATS · CHILDREN'S RESEARCH INSTITUTE · PI LUBAN, NAOMI L · 2016 to 2020
$2.0M
NCATS NIH HHS KL2 TR001877NICHD NIH HHS P50 HD103556NIDCD NIH HHS R01 DC014489
6 · The paper itself

Abstract

Background: Language and the ability to communicate effectively are key factors in mental health and well-being. Despite this critical importance, research on language is limited by the lack of a scalable phenotyping toolkit. Methods: Here, we describe and showcase Lingo - a flexible online battery of language and nonverbal reasoning skills based on seven widely used tasks (COWAT, picture narration, vocal rhythm entrainment, rapid automatized naming, following directions, sentence repetition, and nonverbal reasoning). The current version of Lingo takes approximately 30 minutes to complete, is entirely open source, and allows for a wide variety of performance metrics to be extracted. We asked > 1,300 individuals from multiple samples to complete Lingo, then investigated the validity and utility of the resulting data. Results: We conducted an exploratory factor analysis across 14 features derived from the seven assessments, identifying five factors. Four of the five factors showed acceptable test-retest reliability (Pearson's R > 0.7). Factor 2 showed the highest reliability (Pearson's R = 0.95) and loaded primarily on sentence repetition task performance. We validated Lingo with objective measures of language ability by comparing performance to gold-standard assessments: CELF-5 and the VABS-3. Factor 2 was significantly associated with the CELF-5 "core language ability" scale (Pearson's R = 0.77, p-value < 0.05) and the VABS-3 "communication" scale (Pearson's R = 0.74, p-value < 0.05). Factor 2 was positively associated with phenotypic and genetic measures of socieconomic status. Interestingly, we found the parents of children with language impairments had lower Factor 2 scores (p-value < 0.01). Finally, we found Lingo factor scores were significantly predictive of numerous psychiatric and neurodevelopmental conditions. Conclusions: Together, these analyses support Lingo as a powerful platform for scalable deep phenotyping of language and other cognitive abilities. Additionally, exploratory analyses provide supporting evidence for the heritability of language ability and the complex relationship between mental health and language.

Identifiers

PMID38585791
PMCPMC10996758

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.