Abstract 280: Cerebral Blood Flow and Oxygenation During CPR in Swine
Bibliographic record
Abstract
Introduction: Near-infrared spectroscopy (NIRS) has been used during cardiac arrest (CA) to non-invasively measure cerebral oxygenation. Studies have suggested that survival and neurological outcomes can be predicted using cerebral oxygenation, however, the physiology underlying cerebral oxygenation during CA is poorly understood. This exploratory study examined the behavior of cerebral blood flow and oxygenation in response to standard CPR (SCPR) and CPR with Active decompression and lift (ACD) and an impedance threshold device (ITD). Methods: Ventricular fibrillation was electrically induced in eight domestic swine. Following six minutes of untreated VF, SCPR (100 cpm at 20% anterior-posterior distance) was performed for six minutes. ACD+ITD was performed for an additional six minutes at the same rate and depth as SCPR, but with an additional 20% anterior-posterior distance of active lift. Cerebral blood flow was measured via microspheres and cerebral oxygenation was measured using NIRS. Animals were classified as responders if cerebral oxygenation increased within two minutes of initiating SCPR. Cerebral oxygenation did not change or declined during SCPR in non-responders. Results: Six out of eight animals were classified as responders. Cerebral blood flow and oxygenation increased with ACD+ITD compared to SCPR in responders but failed to increase for non-responders. Cerebral blood flow and oxygenation were lower during baseline (BL) in non-responders (Figure). Conclusions: ACD+ITD appears to improve cerebral blood flow and oxygenation. Changes in cerebral oxygenation during CA appear to be associated with measured changes in cerebral blood flow. NIRS may be clinically useful in estimating oxygenation and blood flow during low blood flow states and monitoring physiological responses to different interventions. Failure of improvement in NIRS during CPR may predict poor outcomes and/or the need for additional interventions guided by NIRS.
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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".