Initiation of passive cooling at referring centre is most predictive of achieving early therapeutic hypothermia in asphyxiated newborns
Bibliographic record
Abstract
OBJECTIVE: To identify factors associated with early initiation and achievement of therapeutic hypothermia (TH) in newborns with hypoxic-ischemic encephalopathy (HIE). METHODS: Retrospective cohort study of newborns who received TH according to National Institute of Child Health and Human Development (NICHD) criteria in two academic level 3 Neonatal Intensive Care Units (NICU) between 2009 and 2013. All infants were transported by a neonatal transport team (NNTT). Multivariate linear regression including who initiated cooling and degree of resuscitation in the model was performed. RESULTS: Two hundred and seven infants were included. Waiting for advice from a tertiary care NICU was independently associated with a 50 minute delay in the median time of initiation of TH. The need for extensive resuscitation (cardiopulmonary resuscitation [CPR] or epinephrine) was independently associated with a reduction of 43 minutes in the median time to reach target core temperature. Log-transformed time to initiation of TH was associated with time to reach target core temperature (P<0.001). A doubling of time to initiation of TH corresponds to a 24% (95% CI 18% to 30%) increase in median time to reach target core temperature. CONCLUSIONS: Initiating passive cooling at the referring centre, before transfer, is critical to faster achievement of target core temperature in asphyxiated infants. Greater outreach education and development of clinical care pathways are needed to improve optimal delivery of TH to enhance outcome.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".