Hemiplegic Cerebral Palsy: Clinical Features Associated With Arterial Ischemic Stroke or Periventricular Venous Infarction
Bibliographic record
Abstract
Objective We sought to determine the clinical features of hemiplegic cerebral palsy associated with perinatal arterial ischemic stroke or periventricular venous infarction. Methods We studied children with hemiplegic cerebral palsy enrolled at 9 rehabilitation centers across Ontario. We compared children with underlying perinatal arterial ischemic stroke or periventricular venous infarction on clinically acquired brain imaging. Analysis also included prenatal (maternal, prenatal/gestational) and perinatal (obstetrical, neonatal) clinical features collected from birth records and standardized parent interviews. Results The 144 children with hemiplegic cerebral palsy (62% male) included 95 with perinatal arterial ischemic stroke and 49 with periventricular venous infarction. In this cohort of children with hemiplegic cerebral palsy, we found neonatal systemic thrombosis (ie, blood clots in the body) ( P = .05), emergency cesarean section ( P = .05), and neonatal seizures ( P = .01) to be clinical features associated with hemiplegic cerebral palsy in children with perinatal arterial ischemic stroke more often than periventricular venous infarction. Preterm delivery rates were similar for perinatal arterial ischemic stroke and periventricular venous infarction. Conclusion We determined clinical features associated with the 2 most typical forms of focal ischemic brain injury in children with hemiplegic cerebral palsy, including mode of delivery emergency cesarean section, neonatal seizures and systemic thrombosis. These findings provide further insight and support for existing findings about focal brain injury patterns leading to hemiplegic cerebral palsy in 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".