Cerebral hemodynamics after cardiac arrest: implications for clinical management
Bibliographic record
Abstract
Following resuscitation from cardiac arrest, hypoxic ischemic brain injury (HIBI) ensues, which is the primary determinant of adverse outcome. The pathophysiology of HIBI can be compartmentalized into primary and secondary injury, resulting from cerebral ischemia during cardiac arrest and reperfusion following successful resuscitation, respectively. During the secondary injury phase, increased attention has been directed towards the optimization of cerebral oxygen delivery to prevent additive injury to the brain. During this phase, cerebral hemodynamics are characterized by early hyperemia following resuscitation and then a protracted phase of cerebral hypoperfusion termed "no-reflow" during which additional hypoxic-ischemic injury can occur. As such, identification of therapeutic strategies to optimize cerebral delivery of oxygen is at the forefront of HIBI research. Unfortunately, randomized control trials investigating the manipulation of arterial carbon dioxide tension and mean arterial pressure augmentation as methods to potentially improve cerebral oxygen delivery have shown no impact on clinical outcomes. Emerging literature suggests differential patient-specific phenotypes may exist in patients with HIBI. The potential to personalize therapeutic strategies in the critical care setting based upon patient-specific pathophysiology presents an attractive strategy to improve HIBI outcomes. Herein, we review the cerebral hemodynamic pathophysiology of HIBI, discuss patient phenotypes as it pertains to personalizing care, as well as suggest future directions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".