Continuous Determination of the Optimal Bispectral Index Value Based on Cerebrovascular Reactivity in Moderate/Severe Traumatic Brain Injury: A Retrospective Observational Cohort Study of a Novel Individualized Sedation Target
Bibliographic record
Abstract
BACKGROUND: We have sought to develop methodology for deriving optimal bispectral index (BIS) values (BISopt) for patients with moderate/severe traumatic brain injury, using continuous monitoring of cerebrovascular reactivity and bispectral electroencephalography. METHODS: Arterial blood pressure, intracranial pressure, and BIS (a bilateral measure that is associated with sedation state) were continuously recorded. The pressure reactivity index, optimal cerebral perfusion pressure (CPPopt), and BISopt were calculated. Using BIS values and the pressure reactivity index, a curve fitting method was applied to determine the minimum value for the pressure reactivity index thus giving the BISopt. RESULTS AND CONCLUSIONS: Identification of BISopt was possible in all of the patients, with both visual inspection of data and using our method of BISopt determination, demonstrating a similarity of median values of 44.62 (35.03–59.98) versus 48 (39.75–57.50) (p = 0.1949). Furthermore, our method outperformed common CPPopt curve fitting methods applied to BISopt with improved percent (%) yields on both the left side 52.1% (36.3–72.4%) versus 31.2% (23.0–48.9%) (p < 0.0001) and the right side 54.1% (35.95–75.9%) versus 33.5% (12.5–47.9%) (p < 0.0001). The BIS values and BISopt were compared with cerebral perfusion pressure, mean arterial pressure, and CPPopt. The results indicated that BISopt’s impact on pressure reactivity was distinct from CPPopt, cerebral perfusion pressure, or mean arterial pressure. Real-time BISopt can be derived from continuous physiologic monitoring of patients with moderate/severe traumatic brain injury. This BISopt value appears to be unassociated with arterial blood pressure or CPPopt, supporting its role as a novel physiologic metric for evaluating cerebral autoregulation. BISopt management to optimize cerebrovascular pressure reactivity should be the subject of future studies in moderate/severe traumatic brain injury.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".