Cerebral Autoregulation Monitoring in Traumatic Brain Injury: An Overview of Recent Advances in Personalized Medicine
Bibliographic record
Abstract
Impaired cerebral autoregulation (CA) in moderate/severe traumatic brain injury (TBI) has been identified as a strong associate with poor long-term outcomes, with recent data highlighting its dominance over cerebral physiological dysfunction seen in the acute phase post-injury. With advances in bedside continuous cerebral physiological signal processing, continuously derived metrics of CA capacity have been described over the past two decades, leading to improvements in cerebral physiological insult detection and development of novel personalized approaches to TBI care in the intensive care unit (ICU). This narrative review focuses on highlighting the concept of continuous CA monitoring and consequences of impairment in moderate/severe TBI. Further, we provide a comprehensive description and overview of the main personalized cerebral physiological targets, based on CA monitoring, that are emerging as strong associates with patient outcomes. The CA-based personalized targets, such as optimal cerebral perfusion pressure (CPPopt), lower/upper limit of regulation (LLR/ULR), and individualized intracranial pressure (iICP) are positioned to change the way we care for patients with TBI in the ICU, moving away from the "one treatment fits all" paradigm of current guideline-based therapeutic approaches toward a true personalized medicine approach tailored to the individual patient. Future perspectives regarding research needs in this field are also discussed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".