Relationship Between Brain Pulsatility and Cerebral Perfusion Pressure: Replicated Validation Using Different Drivers of CPP Change
Bibliographic record
Abstract
Determination of relationships between transcranial Doppler (TCD)-based spectral pulsatility index (sPI) and pulse amplitude (AMP) of intracranial pressure (ICP) in 2 groups of severe traumatic brain injury (TBI) patients (a) displaying plateau waves and (b) with unstable mean arterial pressure (MAP). We retrospectively reviewed patients with severe TBI and continuous TCD monitoring displaying either plateau waves or unstable MAP from 1992 to 1998. We utilized linear and nonlinear regression techniques to describe both cohorts: cerebral perfusion pressure (CPP) versus AMP, CPP versus sPI, mean ICP versus ICP AMP, mean ICP versus sPI, and AMP versus sPI. Nonlinear regression techniques were employed to analyze the relationships with CPP. In plateau wave and unstable MAP patients, CPP versus sPI displayed an inverse nonlinear relationship (R 2 = 0.820 vs. R 2 = 0.610, respectively), with the CPP versus sPI relationship best modeled by the following function in both cases: PI = a + (b/CPP). Similarly, in both groups, CPP versus AMP displayed an inverse nonlinear relationship (R 2 = 0.610 vs. R 2 = 0.360, respectively). Positive linear correlations were displayed in both the plateau wave and unstable MAP cohorts between: ICP versus AMP, ICP versus sPI, AMP versus sPI. There is an inverse relationship through nonlinear regression between CPP versus AMP and CPP versus sPI display. This provides evidence to support a previously-proposed model of TCD pulsatility index. ICP shows a positive linear correlation with AMP and sPI, which is also established between AMP and sPI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".