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Record W4415690696 · doi:10.7860/jcdr/2025/78478.22075

Effects of Anaesthetic Techniques on Cancer Recurrence and Survival: A Systematic Review and Meta-analysis

2025· article· en· W4415690696 on OpenAlexaboutno aff
Hemlata Hemlata, Aparna Shukla, Reetu Verma

Bibliographic record

VenueJOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsnot available
Fundersnot available
KeywordsCancerHazard ratioFunnel plotGeneral anaesthesiaConfidence intervalRetrospective cohort studyCancer surgeryPublication bias

Abstract

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Introduction: Many anaesthetic agents and techniques are used in surgical oncology, yet their effects on cancer cells remain inconclusive. Some studies suggest that propofol-based Total Intravenous Anaesthesia (TIVA) and regional anaesthetic techniques may protect against cancer progression, while volatile anaesthetics and high-dose opioids could increase recurrence. Other research indicates that anaesthetics may have no effect. Aim: To evaluate the impact of General Anaesthesia (GA) supplemented with Regional Analgesia (RA) on Overall Survival (OS) in adult cancer surgery patients, as well as the effects of TIVA versus Inhalational Anaesthesia (INHA) on OS. Materials and Methods: The present systematic review and meta-analysis was conducted at King George’s Medical University, Lucknow, Uttar Pradesh, India, PUBMED/MEDLINE, EMBASE/Emtree, Google Scholar, and ResearchGate were searched. Original retrospective studies from 2005 to 2020 on adult cancer surgeries were included. Eligible studies involved primary cancer surgeries under general anaesthesia (TIVA or volatile) alone or with regional anaesthesia. Only studies published in English and providing numeric Hazard Ratios (HR) were considered. The selected studies were divided into two groups: 10 studies comparing GA with or without RA, and 13 studies comparing TIVA versus INHA. Forest plots were generated to analyse pooled data, and Begg’s funnel plots were used to assess publication bias. The Risk Ratio (RR) was used to measure dichotomous outcomes with 95% confidence intervals (CI). Heterogeneity was assessed using the τ² statistic, and a fixed-effects model was applied. The risk of bias in included studies was assessed using the Newcastle-Ottawa Scale (NOS). The GRADE system was applied to ascertain the level of evidence. Results: A total of 23 retrospective studies involving 67,550 patients were included. In the GA+RA versus GA comparison (10 studies, 46,425 patients), GA+RA was associated with improved overall survival (RR=0.91, 95% CI: 0.89-0.92) but showed no significant difference in recurrence-free survival (RR=1.01, 95% CI: 1.00-1.02). In the TIVA versus INHA comparison (13 studies, 21,125 patients), TIVA was associated with modestly better overall survival (RR=1.11, 95% CI: 1.09-1.13) and recurrencefree survival (RR=1.06, 95% CI: 1.04-1.08). Heterogeneity was low to moderate, and the quality of evidence was graded as low to moderate due to the retrospective design. All 23 studies were evaluated using NOS, with scores ranging from 6 to 9. Most studies scored 7 or higher, indicating moderate to high quality. Evidence certainty was rated as low to moderate for both OS and RFS outcomes across comparisons due to the retrospective nature of the studies. Conclusion: This study provides evidence that anaesthetic technique may impact long-term outcomes in patients undergoing cancer surgery. General anaesthesia combined with regional analgesia (GA+RA) was associated with improved OS, although no significant difference was observed in recurrence-free survival (RFS). Additionally, TIVA showed a survival benefit over INHA, with improvements in both OS and RFS. Despite these findings, the overall certainty of evidence is limited by the retrospective design of the included studies.

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)0.007
metaresearch head score (Gemma)0.034
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.673
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.148
GPT teacher head0.522
Teacher spread0.374 · 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 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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