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Record W4408662755 · doi:10.4097/kja.25073

Revisiting anesthesia-induced preconditioning for neuroprotection in the aging brain: a narrative review

2025· review· en· W4408662755 on OpenAlexaff
Woosuk Chung, Beverley A. Orser

Bibliographic record

VenueKorean journal of anesthesiology · 2025
Typereview
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Research Foundation of KoreaMinistry of Health and Welfare
KeywordsMedicineNeuroprotectionAnesthesiaNarrative reviewIntensive care medicinePharmacology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.925
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.100
GPT teacher head0.398
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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