Neuropsychological Profiles of Written Expression Learning Disabilities Determined by Concordance-Discordance Model Criteria
Bibliographic record
Abstract
Children with specific learning disabilities (SLD) have disparate neuropsychological processing deficits that interfere with academic achievement in spelling, writing fluency, and/or written expression (WE). Although there are multiple potential causes of WE SLD, there is a paucity of research exploring this critical academic skill from a neuropsychological perspective. This study examined the neuropsychological profiles of WE SLD subtypes defined using the concordance-discordance model (C-DM) of SLD identification. Participants were drawn from a sample of 283 children (194 boys, 89 girls) aged 6 years to 16 years old (M(age) = 9.58 years, SD = 2.29 years) referred for comprehensive neuropsychological evaluations in school settings and subsequently selected based on C-DM determined spelling, writing fluency, and WE SLD. WE SLD subtypes differed on several psychomotor, memory, and executive function measures (F range = 2.48-5.07, p range = .049 to <.001), suggesting that these children exhibit distinct patterns of neuropsychological processing strengths and weaknesses. Findings have relevance for differential diagnosis of WE subtypes, discriminating WE SLD subtypes from low WE achievement, and developing differentiated evidence-based instruction and intervention for children with WE SLD. Limitations and future research will be addressed.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".