Multidisciplinary
Bibliographic record
Abstract
Introduction: Ketamine, an N-methyl D-aspartate (NMDA) receptor antagonist, carries potential benefit in patients with neurological illness. The cerebrovascular/cerebral blood flow (CBF) response to ketamine has been poorly outlined in the literature. Methods: We performed a systematic review of the literature on the cerebrovascular/CBF effects of ketamine in both animal and human subjects. Results: We identified 38 animal studies, and 20 human studies. Within the animal studies, a variety of different models were utilized with the majority focusing changes in global CBF or regional cerebral blood flow (rCBF). Overall, ketamine led to an increase in either global CBF or rCBF, with a vasodilatory effect in medium cerebral vessels. With the human studies a total of 379 patients, 107 of which were control subjects, were studied. Most studies focused on either 131Xe CT or PET imaging with ketamine administration. There was a trend to an increase in global CBF and rCBF with ketamine administration. Conclusions: Animal models indicate an increase in global CBF and rCBF with ketamine administration. Human studies display an Oxford 2b, GRADE C, level of evidence to support an increase in global CBF and rCBF with ketamine administration in both healthy volunteers and elective surgical patients without neurological illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.483 | 0.184 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".