P.089 Characteristics of children with cerebral palsy secondary to intrapartum asphyxia in the post-therapeutic hypothermia era
Bibliographic record
Abstract
Background: We explored the profile of children with cerebral palsy (CP) secondary to intrapartum asphyxia (IAP), who were treated with therapeutic hypothermia (TH). We compared neonatal characteristics between children treated with TH with a mild vs severe CP. Methods: We collected perinatal and outcome measures of children treated with TH for IAP. We searched the literature for characteristics of children prior to TH to compare to our cohort. We subdivided our cohort into mild vs. severe CP and compared neonatal characteristics to identify predictors of severe phenotype. Results: We found more children with severe (19/30) compared to mild CP (11/30). Post-TH era children leaned towards a more severe phenotype compared to prior to TH. Children with severe CP had significantly higher mean birth weight, lower 5- and 10-minute Apgars, and more often white matter with associated deep gray matter injury or near-total injury pattern on MRI compared to the mild phenotype group (all p<0.05). Conclusions: Our data leaned to a more severe CP in cooled children compared to pre-TH. Birthweight, 5- and 10-minute Apgars and MRI findings were significantly different between our mild vs severe group. 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) | 0.003 | 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".