Characteristics of Children With Cerebral Palsy in the Post–Therapeutic Hypothermia Era
Bibliographic record
Abstract
Objectives: To explore the profile of children with cerebral palsy secondary to intrapartum asphyxia treated with therapeutic hypothermia after birth and to compare characteristics of children treated with therapeutic hypothermia with mild vs severe cerebral palsy outcome. Study Design: We identified all children treated with therapeutic hypothermia for intrapartum asphyxia in a single-center tertiary-level neonatal intensive care unit from 2008 to 2018 with a cerebral palsy outcome. We collected perinatal and outcome measures from patient charts. We searched the literature for characteristics of children with cerebral palsy prior to therapeutic hypothermia (historical cohort) to compare to our cohort. We subdivided our cohort into mild vs severe cerebral palsy and compared neonatal characteristics to identify predictors of severe phenotype. Results: Thirty of 355 cooled neonates (8%) developed cerebral palsy. More children had spastic quadriparesis and epilepsy, and fewer had visual impairment in the post–therapeutic hypothermia era compared to the historical cohort, but had similar Gross Motor Function Classification System scores. In our cohort, more children had severe (19 of 30, 63%) compared to mild cerebral palsy (11 of 30, 37%). The severe group had higher mean birth weight, lower 5- and 10-minute Apgar scores, and more often white matter injury with associated deep gray matter injury or near-total injury pattern ( P < .05). Conclusions: Our data demonstrated more infants with severe rather than mild cerebral palsy in our cohort treated with therapeutic hypothermia. Birthweight, 5- and 10-minute Apgar scores, and magnetic resonance imaging (MRI) findings were significantly different between mild and severe phenotype groups. Our findings can guide clinicians how to better weigh these factors, when counseling parents in the neonatal period.
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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".