Anesthetics: from modes of action to unconsciousness and neurotoxicity
Bibliographic record
Abstract
Modern anesthetic compounds and advanced monitoring tools have revolutionized the field of medicine, allowing for complex surgical procedures to occur safely and effectively. Faster induction times and quicker recovery periods of current anesthetic agents have also helped reduce health care costs significantly. Moreover, extensive research has allowed for a better understanding of anesthetic modes of action, thus facilitating the development of more effective and safer compounds. Notwithstanding the realization that anesthetics are a prerequisite to all surgical procedures, evidence is emerging to support the notion that exposure of the developing brain to certain anesthetics may impact future brain development and function. Whereas the data in support of this postulate from human studies is equivocal, the vast majority of animal research strongly suggests that anesthetics are indeed cytotoxic at multiple brain structure and function levels. In this review, we first highlight various modes of anesthetic action and then debate the evidence of harm from both basic science and clinical studies perspectives. We present evidence from animal and human studies vis-à-vis the possible detrimental effects of anesthetic agents on both the young developing and the elderly aging brain while discussing potential ways to mitigate these effects. We hope that this review will, on the one hand, invoke debate vis-à-vis the evidence of anesthetic harm in young children and the elderly, and on the other hand, incentivize the search for better and less toxic anesthetic compounds.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".