Revisiting anesthesia-induced preconditioning for neuroprotection in the aging brain: a narrative review
Bibliographic record
Abstract
The growing number of older adults undergoing surgery necessitates that we address the adverse effects of overt and covert perioperative stroke. Preclinical studies have suggested that anesthesia-induced preconditioning may provide neuroprotection by preserving mitochondrial function, activating cytosolic signaling pathways, and reducing neuroinflammation. However, these promising findings from animal studies have not yet translated into improved clinical outcomes. The discordance between preclinical and clinical outcomes may be due to age-related mitochondrial dysfunction and other comorbidities in older human populations, which reduce the effectiveness of anesthetic preconditioning. Mitochondria, which are central to the effectiveness of preconditioning, may be therapeutic targets to restore the neuroprotective effects of anesthetic preconditioning in the aging brain. Emerging evidence suggests that physical prehabilitation, a key component of Enhanced Recovery After Surgery programs, may influence mitochondrial function and could thus, restore anesthesia-induced preconditioning. Although further research is needed to determine the impact of physical prehabilitation on mitochondrial function and anesthetic preconditioning, incorporating physical prehabilitation into perioperative care might enhance neurological outcomes for older patients undergoing surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".