High-Frequency Analysis of the Cerebral Physiological Impact of Ketamine in Acute Traumatic Neural Injury
Bibliographic record
Abstract
Acute traumatic neural injury, also known as traumatic brain injury (TBI), is a leading cause of death. TBI treatment focuses on the use of sedatives, vasopressors, and invasive intracranial pressure (ICP) monitoring to mitigate ICP elevations and maintain cerebral perfusion pressure (CPP). While common sedatives such as propofol and fentanyl have significant side effects, ketamine is an attractive alternative due to its rapid onset and cardiovascular stability. Despite these benefits, ketamine’s use remains controversial due to historical concerns about increasing ICP. Using high-frequency monitoring, this retrospective study compared cerebral pressure-flow dynamics in patients with moderate/severe TBI who received ketamine with those who did not. Statistical analysis included descriptive statistics, comparisons within and between patients receiving ketamine, and evaluation of physiological response around incremental dose changes in ketamine. Various cerebral physiological indices were analyzed, including ICP, CPP, regional cerebral oxygen delivery, intracranial compliance, and cardiovascular reactivity metrics. A total of 122 patients were studied, with 17 receiving ketamine (median age: 37 years) and 105 not receiving ketamine (median age: 42 years). Results indicated higher median ICP in the ketamine group compared with the no ketamine group (9.05 mmHg and 14.00 mmHg, respectively, p = 0.00017); however, this is likely due to differences in patient characteristics and injury severity between the groups. No significant differences were observed in any other index of cerebral pressure-flow dynamics or between any incremental dose change condition. These findings suggest that ketamine does not significantly impact cerebral pressure-flow dynamics, challenging historical concerns about its use in patients with TBI.
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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 | 0.000 | 0.001 |
| 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 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".