Neurodevelopmental profiles of children with unilateral cerebral palsy associated with middle cerebral artery and periventricular venous infarctions
Bibliographic record
Abstract
AIM: To compare the neurodevelopment of children with unilateral cerebral palsy (CP) with middle cerebral artery (MCA) and periventricular venous infarctions (PVIs). METHOD: In this cross-sectional study, children with unilateral CP completed a neurological exam, unimanual Quality of Upper Extremity Skills Test, hand usage questionnaires, and IQ test. Neuroimaging was obtained from health records. RESULTS: Two hundred and forty-five participants with unilateral CP had neuroimaging (151 [61.9%] male, ages 2-18y, median=7y 6mo, interquartile range [IQR]=6y 7mo, with 93.6% in Gross Motor Function Classification System level I/II and 78.8% in Manual Ability Classification System level I/II). Ninety-seven (39.6%) had MCA injuries and 106 (43.3%) had periventricular white matter injuries, of which 48 (45.3%) were PVIs. Median Quality of Upper Extremity Skills Test for the MCA group was 49.2 (IQR=55.8), PVI 79.9 (IQR=23.6) (Mann-Whitney U=988.50, p<0.001). Bimanual hand usage (Children's Hand-use Experience Questionnaire) (Mann-Whitney U=425, p<0.001) and light touch (odds ratio=9.12, 95% confidence interval 1.28-400.76, Fisher's exact test p=0.017) were lower in the MCA compared to the PVI group. Full-scale IQ median centile score for the MCA group was 18.0 (IQR=35.5) and 50.0 (IQR=30.0) for the PVI group (Mann-Whitney U=382, p<0.001). INTERPRETATION: Children with unilateral CP and MCA injuries demonstrated lower hand function and usage, decreased light touch, and lower IQs compared to the PVI group. This study aids in defining rehabilitation needs informed by brain injury patterns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".