Current and Developing Approaches for Facilitating Emergence from General Anesthesia
Bibliographic record
Abstract
Despite the widespread use of clinical anesthesia, the process of emergence from general anesthesia remains primarily driven by anesthetic elimination. Although emergence from general anesthesia is typically safe, prolonged delays strain resource-intensive settings and contribute to increased healthcare costs. In addition to improving access to care, providing clinicians with more precise control over emergence could offer diagnostic potential and improve patient outcomes. For decades, this unmet need has motivated research into the mechanisms underlying anesthetic emergence. Now, the first agents for facilitating emergence are entering the market, with more in development. This narrative review critically evaluates advancements in the development of emergence-promoting therapies, examining insights from preclinical research to clinical trials. This study categorizes prospective emergence agents/strategies into one of three primary approaches: (1) strategies that primarily manipulate anesthetic pharmacokinetics, (2) agents designed to directly target anesthetic receptor-binding sites, and (3) strategies that leverage arousal-promoting neural pathways. The parallel development of these approaches, each with their distinct strengths and limitations, holds promise for paving the way for a tailored approach to facilitate emergence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".