Language abilities in children and adolescents with DLD and ADHD: A scoping review
Bibliographic record
Abstract
PURPOSE: There is an emerging view that attention-deficit/hyperactivity disorder (ADHD) is marked by problems with language difficulties, an idea reinforced by the fact that ADHD is highly comorbid with developmental language disorder (DLD). This scoping review provides an overview of literature on language abilities in children with DLD and ADHD while highlighting similarities and differences. METHOD: A comprehensive search was performed to examine the literature on language abilities in the two disorders, yielding a total of 18 articles that met the inclusion criteria for the present review. Qualitative summaries are provided based on the language domain assessed. RESULTS: The current literature suggests children and adolescents with ADHD have better morphosyntax/grammar, general/core language abilities, receptive, and expressive abilities than those with DLD. Further, that performance is comparable on assessments of semantic and figurative language but varies by sample on assessments of phonological processing, syntax, narrative language, and vocabulary. CONCLUSION: Evidence presented points to children and adolescents with DLD as having greater language difficulties compared to those with ADHD, but with some important caveats. Despite limitations related to the paucity of studies and inconsistencies in how the two types of disorders are identified, our review provides a necessary and vital step in better understanding the language profiles of these two highly prevalent childhood disorders. These findings are useful in optimizing language outcomes and treatment efficacy for children and adolescents with ADHD and DLD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".