Impact of sedation and vasopressors on in vivo cerebrovascular reactivity in adult moderate/severe traumatic brain injury (TBI)
Bibliographic record
Abstract
Traumatic brain injury (TBI) is one of the leading causes of death and disability both in Canada and worldwide. Despite TBIs’ extensive impact on healthcare there has been limited overall improvement over the past four decades. However, the secondary injuries associated with TBI (those being the cascading physiological events that lead to increased tissue damage) are theoretically targetable by critical care. Of these secondary injuries, cerebral autoregulation (CA; the innate ability to maintain healthy homeostasis of cerebral blood flow) has gained extensive interest given it has been independently linked to mortality and poor outcomes. CA is often measured through the surrogate measure of cerebrovascular reactivity (CVR) and has been assessed in TBI related critical care. To date, current care appears to have a limited impact on CVR, with many patients having extensive periods of care dominated by impaired CVR. This has sparked interest in the mediation of CVR, though there is still a lot unknown surrounding the drivers of impaired CVR after TBI. Thus, this thesis aims to help answer the following questions: a) what is the relationship between current guideline-based agents (vasopressor and sedative agents) administered in vivo and CVR? and b) what is the relationship between objectively measured depth of sedation and CVR/cerebral physiologies? This work evaluated several cerebral physiologic indices and CVR measures with in vivo sedative and vasopressor agents. Current critical care guideline-driven use of vasopressor and sedative agents has a limited impact on CVR measures, when evaluated in a dose-based fashion with high-frequency cerebral physiology. Moreover, impaired CVR measures appear to be relatively treatment independent from current care. Additionally, this work explored the relationship between objective depth of sedation and CVR. Such work documented a unique relationship between these measures, where CVR can in theory be mediated by the depth of sedation within a patient, leveraging the independent dose-response of sedative agents on neurovascular coupling. This work provides the potential foundation for future assessment of CVR measures and its mediation through the novel relationship between depth of sedation. Though like any work in this field, conclusions drawn remain preliminary and much further work is required.
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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 | 0.004 |
| 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 | 0.001 | 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".