Continuous personalized cerebrovascular reactivity-based physiologic metrics in neurocritical care: a narrative review of the current landscape, limitations, and future directions
Bibliographic record
Abstract
Over the past several decades, significant progress has been made in our ability to achieve guideline-based cerebral physiologic targets for the management of moderate-to-severe traumatic brain injury (TBI). However, despite these advancements, there has been limited improvement in the long-term outcomes associated with this condition. It has been suggested that this is in part due to the generalized approach of current Brain Trauma Foundation guidelines. It has been demonstrated that significant heterogeneity in cerebral physiologic response to TBI exists between patients, and that it involves highly dynamic physiologic mechanisms which vary across a patient's time in the ICU. Therefore, an individualized management approach, that accounts for individual phenotype, injury heterogeneity, and the dynamic nature of cerebral physiology, is urgently needed. Recently, multiple personalized physiologic metrics, based on cerebrovascular reactivity optimization, have been proposed as potential tools to help address this increasingly important issue. These include the cerebral perfusion pressure optimum (CPPopt), mean arterial pressure optimum (MAPopt), bispectral index optimum (BISopt), and individualized intracranial pressure (iICP) thresholds. These metrics aim to shift neurocritical care management from static, population-based targets to dynamic, personalized targets that are tailored to a patient's real-time cerebral physiologic needs. In this narrative review, we will cover the topic of continuously derived cerebrovascular reactivity-based personalized physiologic metrics in neurocritical care, including the current states of the various existing techniques, their limitations, and future directions.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".