Regional anesthesia might reduce recurrence and metastasis rates in adult patients with cancers after surgery: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: The influence of anesthesia techniques on cancer recurrence and metastasis following oncological surgery is a topic of growing interest. This meta-analysis investigates the potential effects of regional anesthesia (RA), either independently or combined with general anesthesia (GA), on these outcomes. METHODS: We performed an extensive search across PubMed, Embase, and the Cochrane Library databases. The primary outcome was cancer recurrence, while the secondary outcomes were local recurrence and distant metastasis. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated by utilizing random-effects models. The Newcastle-Ottawa Scale (NOS) was used for quality assessment of observational studies, the Cochrane Risk of Bias Tool for Randomized Trials (Rob 2.0) was used for randomized controlled trials, and all the outcomes were assessed by using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE). RESULTS: This study included 32 studies comprising 24,724 cancer patients. RA, either alone or in combination with GA, was significantly associated with reduced cancer recurrence compared to GA alone (OR = 0.82; 95% CI = 0.72 to 0.94; p < 0.01). This association remained significant for prostate cancer patients in subgroup analyses (OR = 0.71; 95% CI = 0.51 to 0.98; p = 0.04) and in the context of epidural anesthesia combined with GA. However, there were no significant associations noted for local recurrence or distant metastasis. CONCLUSIONS: This meta-analysis provides evidence that RA, used alone or adjunctively with GA, is associated with a lower risk of cancer recurrence, particularly in patients with prostate cancer. However, no significant effects were observed on local recurrence or distant metastasis. Further prospective studies should be conducted to clarify this important issue.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.066 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".