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Impact of General Anesthetics on Postoperative Infections – A Scoping Review

2025· preprint· en· W4413364945 on OpenAlexaff
Lynn Jazzar, Palak Watts, Christine Lehmann

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

Postoperative infections represent the most frequent complication after surgery. Anesthetic agents, while essential during surgical procedures to ensure sedation or hypnosis, are becoming increasingly recognized as modulators of immune function. Volatile anesthetics have been identified as being able to attenuate the inflammatory response in diverse experimental models. Propofol, a widely used intravenous anesthetic, has also been described to exhibit strong anti-inflammatory mechanisms. This review synthesizes current cellular, experimental, and clinical evidence on the immunomodulatory effects of anesthetic agents, highlighting their impact on host defense mechanisms and postoperative infections. By exploring mechanistic properties and clinical outcomes, it underscores the importance of anesthetic choice in enhancing immune function and postoperative recovery.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.007
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.114
GPT teacher head0.425
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations0
Published2025
Admission routes1
Has abstractyes

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