The use of ketamine as a neuroprotective agent following cardiac arrest: A scoping review of current literature
Bibliographic record
Abstract
AIMS: The objective of this article is to summarize the state of the literature surrounding the use of ketamine as a neuroprotective agent following cardiac arrest. METHODS: Five electronic databases were used to search for studies related to the use of ketamine for neuroprotection following cardiac arrest. This search was performed once in May 2020, and an updated search was conducted in May 2021 and March 2022. RESULTS: All searches combined retrieved 181 results; no clinical trials were identified. As such, the authors were limited to writing a scoping review of the literature rather than a systematic review. CONCLUSIONS: The current state of the literature describes the mechanism of action of ketamine as a neuroprotective agent through its action as an NMDA antagonist. There is evidence of its efficacy as a neuroprotective agent in preclinical models of cardiac arrest. Current published clinical work supports the use of ketamine ameliorating neurologic outcomes in other conditions such as epilepsy, traumatic brain injury, and depression. The current state of the literature is reflective of the notion that the use of ketamine following cardiac arrest may result in improved neurologic outcomes. Future research directions should focus on the use of ketamine as a possible clinical intervention following cardiac arrest.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".