Risk factors for Hemiplegic Cerebral Palsy due to Arterial Ischemic Stroke or Periventricular Venous Infarction
Bibliographic record
Abstract
Abstract authors: Trish Domi¹, Darcy Fehlings², Pradeep Krishnan¹, Mahohar Shroff¹, Matylda Machnowska³, Amanda Robertson¹, Gabrielle deVeber¹, on behalf of the CP-NET Group⁴ ¹Hospital for Sick Children, ²Holland Bloorview Rehabilitation Hospital, ³Sunnybrooke Health Sciences Center, ⁴Holland Bloorview Rehabiliation Center Abstract body: Objective: We sought to determine risk factors for hemiplegic cerebral palsy (HCP) associated with perinatal arterial ischemic stroke (AIS) or periventricular venous infarction (PVI). Methods: We studied children with hemiplegic cerebral palsy (HCP) enrolled at nine rehabilitation centres across Ontario. We compared children with underlying AIS or PVI on clinically acquired brain imaging. In addition, we analyzed prenatal (maternal, prenatal/gestational) and perinatal (obstetrical, neonatal) characteristics collected from birth records and standardized parent interviews. Results: The 144 children with HCP (62% male) included 95 with AIS and 49 with PVI. On multivariate analysis, children with PVI had increased rates of maternal fertility treatment (OR=4.7;95%CI=1.0-28.7;p=0.0379) and decreased rates of neonatal seizures (OR=0.03;95%CI=0.0002-0.255;p=0.0001), systemic blood clots (OR=0.082;95%CI=0.0006-0.795;p=0.0278) and emergency cesarian section (OR=0.3;95%CI=0.093-0.896). Preterm delivery rates were similar for AIS and PVI. Conclusion: We determined novel risk factors differentiating the two most typical forms of focal ischemic brain injury in children with hemiplegic CP. These include fertility treatments, mode of delivery, neonatal seizures and systemic blood clots. These findings provide direction for further research exploring causal pathways of focal brain injury and cerebral palsy.
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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 | 0.000 | 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) | 0.005 | 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".