Intercerebral autoregulation index consistency in the derivation of <scp>CPPopt</scp> , <scp>MAPopt</scp> , and <scp>BISopt</scp> in humans: A scoping review
Bibliographic record
Abstract
The optimization of cerebral perfusion and sedation using autoregulation-derived physiologic targets such as optimal cerebral perfusion pressure (CPPopt), optimal mean arterial pressure (MAPopt), and optimal bispectral index (BISopt) has emerged as a promising strategy in neurocritical and perioperative care. However, the reliability and comparability of these optimal (Opt) parameters across different autoregulatory indices remain uncertain. This paper systematically reviews and synthesizes literature comparing CPPopt, MAPopt, and BISopt derived from invasive and noninvasive indices. Following PRISMA-ScR guidelines, studies directly comparing CPPopt, MAPopt, or BISopt from at least two indices were included. Ten studies compared CPPopt, mostly in traumatic brain injury, with mean values between 70 and 76 mmHg. Nine studies compared MAPopt, reporting strong correlations between transcranial doppler-, near-infrared spectroscopy-, and intracranial pressure-derived indices across populations, though limits of agreement were wide. One study compared BISopt across indices, showing internal consistency, while two cross-Opt studies found little correlation between BISopt and CPPopt or MAPopt. CPPopt and MAPopt appear physiologically robust across indices, supporting translational potential in both invasive and noninvasive settings. BISopt may represent a distinct optimization domain related to sedation rather than perfusion. Methodological heterogeneity and limited outcome validation remain barriers. Future work should emphasize standardization, multimodal integration, and outcome-driven trials.
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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 | 0.030 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".