The Cerebrovascular Response to Ketamine
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to perform a systematic review of the literature on the cerebrovascular/cerebral blood flow (CBF) effects of ketamine in both animal and human subjects. MATERIALS AND METHODS: We searched MEDLINE, BIOSIS, EMBASE, Global Health, SCOPUS, and Cochrane Library from inception to December 2014. Two reviewers independently identified all manuscripts pertaining to the administration of ketamine in both human and animal subjects in which the impact on CBF/cerebral vasculature was recorded by means of functional magnetic resonance imaging, positron emission tomography, single-photon emission computed tomography, xenon computed tomography, transcranial Doppler velocities, arteriovenous difference in N2O method of CBF measurement, cerebral digital subtraction angiography, or any other objective means of CBF determination. RESULTS: We identified 38 animal studies with various animal models studied. Overall there was a trend to a direct vasodilatory effect of ketamine on the cerebral vasculature, with a trend in most studies to an increase or regional CBF (rCBF) or global CBF.Twenty human studies were identified. The majority displayed an increase in rCBF and global CBF on imaging in patients without neurological illness. CONCLUSIONS: Animal models indicate an increase in global CBF and rCBF with ketamine administration, with a trend to vasodilation of medium-sized intracranial vessels through a calcium-dependent mechanism. Human studies display an Oxford 2b, Grading of Recommendation Assessment Development and Education C, level of evidence to support a trend to increased global CBF and rCBF with ketamine administration in both healthy volunteers and elective surgical patients without neurological illness.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".