Corticospinal tract diffusion properties and robotic visually guided reaching in children with hemiparetic cerebral palsy
Bibliographic record
Abstract
Perinatal stroke is the leading cause of hemiparetic cerebral palsy (CP), resulting in life-long disability. In this study, we examined the relationship between robotic upper extremity motor impairment and corticospinal tract (CST) diffusion properties. Thirty-three children with unilateral perinatal ischemic stroke (17 arterial, 16 venous) and hemiparesis were recruited from a population-based research cohort. Bilateral CSTs were defined using diffusion tensor imaging (DTI) and four diffusion metrics were quantified: fractional anisotropy (FA), mean (MD), radial (RD), and axial (AD) diffusivities. Participants completed a visually guided reaching task using the KINARM robot to define 10 movement parameters including movement time and maximum speed. Twenty-six typically developing children underwent the same evaluations. Partial correlations assessed the relationship between robotic reaching and CST diffusion parameters. All diffusion properties of the lesioned CST differed from controls in the arterial group, whereas only FA was reduced in the venous group. Non-lesioned CST diffusion measures were similar between stroke groups and controls. Both stroke groups demonstrated impaired reaching performance. Multiple reaching parameters of the affected limb correlated with lesioned CST diffusion properties. Lower FA and higher MD were associated with greater movement time. Few correlations were observed between non-lesioned CST diffusion and unaffected limb function though FA was associated with reaction time (R = -0.39, p < .01). Diffusion properties of the lesioned CST are altered after perinatal stroke, the degree of which correlates with specific elements of visually guided reaching performance, suggesting specific relevance of CST structural connectivity to clinical motor function in hemiparetic children.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".