Effect of inhalational agents in breast cancer surgery on tumour metastasis: Systematic review and meta-analysis
Bibliographic record
Abstract
Background and Aims: The primary treatment for breast cancer involves surgically removing the affected breast. Different types of anaesthesia used during surgery may have an impact on cancer recurrence. This meta-analysis was conducted to assess the current level of evidence for the impact of the type of anaesthesia for breast cancer surgery on cancer recurrence. Methods: Following PROSPERO registration, a comprehensive database search was conducted across PubMed, Cochrane, Embase, Scopus, and Google Scholar, encompassing all available data up to December 2024. Randomised controlled trials (RCTs) and retrospective studies assessing the effect of inhalational anaesthetic agents on cancer recurrence after breast cancer surgeries were included. The Cochrane Collaboration's risk of bias and the Newcastle-Ottawa scale were used for risk of bias assessment in RCTs and retrospective studies, respectively. We inverted the hazard ratio (HR) to (1/HR) for studies that reported inhalational as the control group. Results: Eight studies were included: four RCTs and four retrospective cohort studies. No association between the anaesthetic agent used and the recurrence of breast cancer was seen in RCTs (risk ratio (RR) =1.07, 95% confidence interval (CI): 0.89, 1.29). Included retrospective cohort studies showed RR = 1.46 (95% CI: 1.32, 1.62), showing an association of recurrence with inhalational agents compared to total intravenous anaesthesia-based or regional anaesthesia with significant bias. Conclusion: The retrospective studies suggest a possible association between inhalational anaesthesia and breast cancer recurrence. However, these findings should be interpreted with caution due to the inherent limitations of retrospective studies, including a higher risk of bias and variability in control group definitions. This contradicts findings from prospective RCTs. Further research is needed to understand the mechanisms behind this association and to weigh the pros and cons of different anaesthesia techniques.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".