Determinants of Outcomes After Head Cooling for Neonatal Encephalopathy
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to evaluate the role of factors that may determine the efficacy of treatment with delayed head cooling and mild systemic hypothermia for neonatal encephalopathy. METHODS: A total of 218 term infants with moderate to severe neonatal encephalopathy plus abnormal amplitude-integrated electroencephalographic recordings, assigned randomly to head cooling for 72 hours, starting within 6 hours after birth (with the rectal temperature maintained at 34.5 +/- 0.5 degrees C), or conventional care, were studied. Death or severe disability at 18 months of age was assessed in a multicenter, randomized, controlled study (the CoolCap trial). RESULTS: Treatment, lower encephalopathy grade, lower birth weight, greater amplitude-integrated electroencephalographic amplitude, absence of seizures, and higher Apgar score, but not gender or gestational age, were associated significantly with better outcomes. In a multivariate analysis, each of the individually predictive factors except for Apgar score remained predictive. There was a significant interaction between treatment and birth weight, categorized as > or =25th or <25th percentile for term, such that larger infants showed a lower frequency of favorable outcomes in the control group but greater improvement with cooling. For larger infants, the number needed to treat was 3.8. Pyrexia (> or =38 degrees C) in control infants was associated with adverse outcomes. Although there was a small correlation with birth weight, the adverse effect of greater birth weight in control infants remained significant after adjustment for pyrexia and severity of encephalopathy. CONCLUSIONS: Outcomes after hypothermic treatment were strongly influenced by the severity of neonatal encephalopathy. The protective effect of hypothermia was greater in larger infants.
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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 | 0.001 | 0.007 |
| 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) | 0.001 | 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".