Quantitative assessment of brain microvascular and tissue oxygenation during cardiac arrest and resuscitation in pigs
Bibliographic record
Abstract
Cardiac arrest is associated with a very high rate of mortality, in part due to inadequate tissue perfusion during attempts at resuscitation. Parameters such as mean arterial pressure and end-tidal carbon dioxide may not accurately reflect adequacy of tissue perfusion during cardiac resuscitation. We hypothesised that quantitative measurements of tissue oxygen tension would more accurately reflect adequacy of tissue perfusion during experimental cardiac arrest. Using oxygen-dependent quenching of phosphorescence, we made measurements of oxygen in the microcirculation and in the interstitial space of the brain and muscle in a porcine model of ventricular fibrillation and cardiopulmonary resuscitation. Measurements were performed at baseline, during untreated ventricular fibrillation, during resuscitation and after return of spontaneous circulation. After achieving stable baseline brain tissue oxygen tension, as measured using an Oxyphor G4-based phosphorescent microsensor, ventricular fibrillation resulted in an immediate reduction in all measured parameters. During cardiopulmonary resuscitation, brain oxygen tension remained unchanged. After the return of spontaneous circulation, all measured parameters including brain oxygen tension recovered to baseline levels. Muscle tissue oxygen tension followed a similar trend as the brain, but with slower response times. We conclude that measurements of brain tissue oxygen tension, which more accurately reflect adequacy of tissue perfusion during cardiac arrest and resuscitation, may contribute to the development of new strategies to optimise perfusion during cardiac resuscitation and improve patient outcomes after cardiac arrest.
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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) | 0.000 | 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".