Clinical Phenotype, Predictors and Early Biomarkers of Dyskinetic Cerebral Palsy Prognosis
Bibliographic record
Abstract
BACKGROUND: Dyskinetic cerebral palsy (DCP) is a severe subtype of cerebral palsy in which children often present substantial functional impairment and multiple comorbidities. Our knowledge of the clinical picture of DCP is limited and our understanding of which markers best predict later impairment is scarce. This study aims to describe the presentation of DCP and examine the value of gestational age (GA) and magnetic resonance imaging (MRI) findings as early markers of eventual DCP prognosis. METHODS: Data from 170 children with DCP were extracted from the Canadian Cerebral Palsy Registry. Participants were classified as preterm or full-term and were divided into two groups based on MRI results: (1) normal/nonspecific, white matter injury, watershed injury, focal insult, malformation and (2) deep grey matter injury, and near total grey matter injury. Pearson Chi-square analyses were carried out to examine how DCP-associated risk factors and comorbidities vary with GA and MRI findings. RESULTS: Most children with DCP are born at term (69%), experience severe motor impairments (70% with Gross Motor Function Classification System and 73% with Manual Ability Classification System Levels IV-V), and present more than one comorbidity (46%). GA is associated with neonatal encephalopathy, hyperbilirubinemia, perinatal adversity, higher Manual Ability Classification System level, epilepsy and deafness (P < 0.01, P = 0.04, P < 0.01, P < 0.01, respectively). MRI findings are associated with neonatal encephalopathy, and perinatal adversity (P = 0.003), but not motor and speech impairment or any of the DCP-associated comorbidities. CONCLUSIONS: DCP is a severe form of CP affecting predominantly term-born infants. Relative to MRI findings, GA is a stronger predictor of eventual DCP prognosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".