A Porcine Model of Neonatal Hypoxia-Asphyxia to Study Resuscitation Techniques in Newborn Infants
Bibliographic record
Abstract
Two to three million newborn infants worldwide need extensive cardiopulmonary resuscitation (CPR), and approximately one million of these infants die annually worldwide. Therefore, resuscitation techniques require further refinement to provide better outcomes. To investigate the effectiveness of various interventions and to understand the pathophysiology and pharmacology of neonatal CPR, it is important to have animal models that reliably reproduce features observed in neonates who require resuscitation. Herein, we describe an experimental animal model in newborn piglets that simulates neonatal asphyxia and enables us to examine resuscitation interventions, reoxygenation, and recovery processes. The newborn piglet has several advantages including similar development to a human fetus at 36–38 week’s gestation, and comparable body systems and body size, allowing for surgical instrumentation, monitoring, and collection of biological samples. Furthermore, using this model of neonatal asphyxia, we are also able to describe an increasingly important clinical situation in the laboratory setting—pulseless electrical activity (PEA). Since the integration of electrocardiogram into the neonatal resuscitation guidelines, there has been an increased awareness of PEA in newborn infants. The animal model we describe can therefore serve as a valuable tool to bridge the knowledge gap and improve the outcome of asphyxiated newborns in the delivery room.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".