Effects of epidural anesthesia on the prognosis of ovarian cancer—a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The global low survival rate among ovarian cancer patients has resulted in significant social and economic burdens. Nevertheless, previous studies have produced mixed results when exploring the link between anesthetic techniques and the prognosis of ovarian cancer. The study aims to compare the effect of epidural anesthesia with general anesthesia on survival time after cytoreductive surgery in patients with ovarian cancer. METHODS: The PubMed (National Library of Medicine), Cochrane library, Web of science, Embase, CNKI (China National Knowledge Internet), Wanfang Med Online (China database), were systematically searched from inception to May, 2023, using the Medical Subject Headings [MeSH] of "Ovarian Neoplasm" and "Anesthesia, Epidural" and free words to identify systematic reviews or meta-analyses. The research methodology involved analyzing randomized controlled trials (RCTs), as well as prospective or retrospective cohort studies, which compared the long-term prognosis of patients with ovarian cancer under general anesthesia combined with epidural anesthesia (GEA) versus general anesthesia alone (GA). The Newcastle Ottawa Scale (NOS) was used to assess methodological quality and bias. Data extraction and assessment of study quality were conducted by two independent reviewers. A meta-analysis was then performed to calculate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). Overall survival (OS) was defined as the primary outcome, time to tumor recurrence (TTR) was the secondary outcome. Epidural anesthesia could be used intraoperatively and immediately postoperatively (EIP), or postoperatively only (EP). GEA includes EIP and EP. RESULTS: = 0%, fixed-effect model, P < 0.05. CONCLUSIONS: It is hard to conclude that postoperative epidural analgesia offers greater benefits than GA. However, general anesthesia combined with epidural anesthesia (EIP) can improve overall survival in ovarian cancer patients, allowing the anesthesiologist to use anesthesia techniques to provide a favorable prognosis for the ovarian cancer patient. Tumor staging and the extent of cell reduction are also critical factors that significantly influence the long-prognosis of ovarian cancer patients.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| 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) | 0.003 | 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".