Language Difficulties in School-Age Children With Developmental Dyslexia
Bibliographic record
Abstract
Developmental dyslexia (DD) is a common reading disability, affecting 5% to 11% of children in North America. Children classified as having DD often have a history of early language delay (ELD) or language impairments. Nevertheless, studies have reported conflicting results as to the association between DD-ELD and the extent of current language difficulties in children with DD. To examine these relationships, we queried the parents of school-age children with reading difficulties on their child’s early and current language ability. Siblings were also examined. Children were directly assessed using quantitative tests of language and reading skills. To compare this study with the literature, we divided the sample ( N = 674) into three groups: DD, intermediate readers (IR), and skilled readers (SR). We found a significant association between DD and ELD, with parents of children in the DD/IR groups reporting their children put words together later than the SR group. We also found a significant association between DD and language difficulties, with children with low reading skills having low expressive/receptive language abilities. Finally, we identified early language predicted current language, which predicted reading skills. These data contribute to research indicating that children with DD experience language difficulties, suggesting early recognition may help identify reading problems.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".