Case Study: Motor Imagery Training Improves Force Control in a Young Female with Developmental Coordination Disorder
Bibliographic record
Abstract
In older and young females, motor imagery training improves force steadiness, a measure related to functional performance. Individuals with developmental coordination disorder may experience functional deficits which can result in difficulties performing tasks of daily living. Improving force steadiness in individuals with developmental coordination disorder could lead to improvements in functional performance. It is unknown if motor imagery training will improve force steadiness in young females with developmental coordination disorder. A young female aged nineteen volunteered for an experiment and disclosed developmental coordination disorder. Subsequently, this participant was compared to older (n=7) and young (n=11) females without developmental coordination disorder. Participants completed a block design. Blocks 1,3,5 included seven elbow flexion force tracking tasks at 10% maximal voluntary contraction. Blocks 2 and 4 included motor imagery training of the force tracking tasks. Corticospinal excitability was recorded within the last 5 seconds of each tracking task in blocks 1,3, and 5. Force steadiness improved across blocks in the female with development coordination disorder similar to older females and corticospinal excitability increased unlike older and young females. The results of this case study highlight motor imagery training could be a beneficial modality for young females with developmental coordination disorder.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".