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Record W7116665105 · doi:10.3390/bs16010012

Cognitive Profiles of Children with Reading Disabilities and/or ADHD

2025· article· en· W7116665105 on OpenAlexaff
Miao Li, John R. Kirby, Wei Zhao

Bibliographic record

VenueBehavioral Sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsQueen's University
Fundersnot available
KeywordsCognitionComorbidityAttention deficit hyperactivity disorderDyslexiaWorking memoryMultivariate analysisReading disabilityReading (process)Intervention (counseling)

Abstract

fetched live from OpenAlex

Building on prior work, this study examined cognitive profiles of children with reading disabilities (RD), attention-deficit/hyperactivity disorder (ADHD), and their comorbidity (ADHD + RD) compared to typically developing (TD) peers. Participants included 151 Grade 1-3 students, where there were 31 students with RD, 43 with ADHD, 27 with ADHD + RD, and 50 TD in China. Children were assessed in four cognitive domains: attention, inhibition, working memory, and rapid automatized naming (RAN), with age statistically controlled. Significant group differences emerged in each domain. The TD group consistently outperformed all groups. The comorbid ADHD + RD group showed pronounced deficits in attention, inhibition, and RAN. One-way ANCOVAs and multivariate analyses indicated that both RD and ADHD groups showed weaknesses in attention and RAN, with RD group weaker in working memory and ADHD group in inhibition. A 2 × 2 factorial ANCOVA confirmed significant main effects of RD and/or ADHD across domains, with no significant interaction effects, supporting an additive model. Findings highlight distinct and overlapping cognitive challenges associated with RD and ADHD and underscore the need for domain-specific intervention planning.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.097
GPT teacher head0.409
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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