Motor proficiency of 6‐ to 9‐year‐old children with speech and language problems
Bibliographic record
Abstract
AIM: This study compared the gross motor skills of school-age children (mean age 7 y 8 mo, range 6-9 y) with developmental speech and language disorders (DSLDs; n=105; 76 males, 29 females) and typically developing children (n=105; 76 males, 29 females). The relationship between the performance parameters and the children's age was investigated as well as the role of the type of DSLD. METHOD: The children with DSLDs were classified by their schools' speech and language therapists into three subgroups: children with speech disorders (n=16), those with language disorders (n=41), or those with both (n=48). They were tested with the Test of Gross Motor Development, 2nd edition. RESULTS: Compared with their typically developing peers, all three DSLD subgroups scored lower on the locomotor (all p values<0.001) and object control sub tests (all p values<0.001). Significant performance differences were found between the three types of DSLD (all p values<0.01) where the children with language disorders only performed better. Older children performed better than the younger ones (plocomotor=0.029, pobject control<0.001), but the magnitude of differences between the children with DSLDs and their peers did not change with increasing age. INTERPRETATION: Children with DSLDs have poor gross motor skills. Although the performance of children with DSLDs improves with increasing age, it lags behind that of typically developing children. The present results emphasize the importance of early diagnosis and intervention for children with motor deficits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".