Characterizing the Motor Skills in Children with Specific Language Impairment
Bibliographic record
Abstract
BACKGROUND/AIMS: Specific language impairment (SLI) is characterized by deficits in language ability. However, studies have also reported motor impairments in SLI. It has been proposed that the language and motor impairments in SLI share common origins. This exploratory study compared the gross, fine, oral, and speech motor skills of children with SLI and children with typical development (TD) to determine whether children with SLI would exhibit difficulties on particular motor tasks and to inform us about the underlying cognitive deficits in SLI. METHODS: A total of 13 children with SLI (aged 8-12 years) and 14 age-matched children with TD were administered the Movement Assessment Battery for Children - Second Edition and the Verbal Motor Production Assessment for Children to examine gross and fine motor skills and oral and speech motor skills, respectively. RESULTS: Children with SLI scored significantly lower on gross, fine, and speech motor tasks relative to children with TD. In particular, children with SLI found movements organized into sequences and movement modifications challenging. On oral motor tasks, however, children with SLI were comparable to children with TD. CONCLUSION: Impairment of the motor sequencing and adaptation processes may explain the performance of children with SLI on these tasks, which may be suggestive of a procedural memory deficit in SLI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".