Cross‐lateralisation in children with attention‐deficit/hyperactivity disorder and motor skill performance
Bibliographic record
Abstract
Cross-lateralisation and increased motor difficulties have been reported in children with attention-deficit/hyperactivity disorder (ADHD). Nevertheless, the question of how crossed (i.e. mixed preference) or uncrossed (i.e. same side preference) lateralisation impacts motor performance in children with ADHD has yet to be examined. In this study, previously validated observational measures of hand and foot preference were used to identify right-handed children with ADHD who display cross- (n = 29) and uncross-lateralisation (n = 31). An uncross-lateralised typically developing (TD) group (n = 32) was also identified, and included as a control. Motor performance was assessed with seven valid and reliable fine and gross motor tasks performed with both preferred and non-preferred limbs. Group, task and sex-related effects were examined. Findings revealed that male (but not female) cross-lateralised children with ADHD performed significantly worse, respectively, in two of the fine motor tasks (spiral tracing [p < .01], and dot filling [p < .05]). Results suggest that cross-lateralised hand and foot preference may affect complex motor skills in male children with ADHD. Furthermore, characteristics of ADHD may manifest differently in male and female children. Findings highlight the importance of considering both hand and foot preference when targeting motor interventions for children with ADHD.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".