A Piglet Perinatal Asphyxia Model to Study Cardiac Injury and Hemodynamics after Cardiac Arrest, Resuscitation, and the Return of Spontaneous Circulation
Bibliographic record
Abstract
Neonatal piglets have been extensively used as translational models for perinatal asphyxia. In 2007, we adapted a well-established piglet asphyxia model by introducing cardiac arrest. This enabled us to study the impact of severe asphyxia on key outcomes, including the time taken for the return of spontaneous circulation (ROSC), as well as the effect of chest compressions according to alternative protocols for cardiopulmonary resuscitation. Due to the anatomical and physiological similarities between piglets and human neonates, piglets serve as good models in studies of cardiopulmonary resuscitation and hemodynamic monitoring. In fact, this cardiac arrest model has provided evidence for guideline development through research on resuscitation protocols, pathophysiology, biomarkers, and novel methods for hemodynamic monitoring. Notably, the incidental finding that a substantial fraction of piglets have pulseless electrical activity (PEA) during cardiac arrest may increase the applicability of the model (i.e., it may be used to study pathophysiology extending beyond the perinatal period). However, the model generation is technically challenging and requires various skill sets, dedicated personnel, and a fine balance of the measures, including the surgical protocols and the use of sedatives/analgesics, to ensure a reasonable rate of survival. In this paper, the protocol is described in detail, as well as experiences with adaptations to the protocol over the years.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".