Effect of Different General Anesthesia Methods on the Prognosis of Patients with Breast Cancer after Resection: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background. The effect of total intravenous anesthesia (TIVA) and inhalation anesthesia (IA) on the prognosis of breast cancer patients has been controversial. The study is aimed at exploring the effects of different anesthesia methods on the postoperative prognosis of breast cancer patients. Methods. Literature retrieval was conducted in PubMed, EMBASE, MEDLINE, Embase, CENTRAL, and CNKI databases. The literature topic was to compare the effects of TIVA and IA on the prognosis of patients undergoing breast cancer resection. Two researchers extracted data from the literature independently. This study included randomized controlled trials that evaluated for risk of bias according to the “Risk assessment Tool for Bias in Randomized Trials” in the Cochrane Manual. The Newcastle-Ottawa Scale (NOS) was used to assess the risk of bias in observational studies. The chi-square test was used for the heterogeneity test. Publication bias was assessed using funnel plots and Egger’s test. If heterogeneity existed between literature, subgroup analysis and sensitivity analysis were used to explore the source of heterogeneity. Sensitivity analysis was performed by excluding low-quality and different-effect models. Data were statistically analyzed using the Cochrane software RevMan 5.3. Hazard ratio (HR) and 95% confidence interval (CI) were used for statistical description. Results. Seven literatures were selected for meta-analysis. There were 9781 patients, 3736 (38.20%) receiving TIVA and 6045 (61.80%) receiving inhalation anesthesia. There was no significant difference in overall survival (OS) between TIVA and IA breast cancer patients ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mtext>HR</a:mtext> <a:mo>=</a:mo> <a:mn>1.05</a:mn> </a:math> , 95% CI (0.91, 1.22), <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>Z</c:mi> <c:mo>=</c:mo> <c:mn>0.70</c:mn> </c:math> , <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>P</e:mi> <e:mo>=</e:mo> <e:mn>0.49</e:mn> </e:math> ). There was no difference in the literature ( <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:msup> <g:mrow> <g:mi>χ</g:mi> </g:mrow> <g:mrow> <g:mn>2</g:mn> </g:mrow> </g:msup> <g:mo>=</g:mo> <g:mn>6.82</g:mn> </g:math> , <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" id="M5"> <i:mi>P</i:mi> <i:mo>=</i:mo> <i:mn>0.34</i:mn> </i:math> , <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" id="M6"> <k:msup> <k:mrow> <k:mi>I</k:mi> </k:mrow> <k:mrow> <k:mn>2</k:mn> </k:mrow> </k:msup> <k:mo>=</k:mo> <k:mn>12</k:mn> <k:mi>%</k:mi> </k:math> ), and there was no obvious publication bias. There was no significant difference in recurrence-free survival (RFS) between TIVA and IA patients ( <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" id="M7"> <m:mtext>HR</m:mtext> <m:mo>=</m:mo> <m:mn>0.95</m:mn> </m:math> , 95% CI (0.79, 1.13), <o:math xmlns:o="http://www.w3.org/1998/Math/MathML" id="M8"> <o:mi>Z</o:mi> <o:mo>=</o:mo> <o:mn>0.61</o:mn> </o:math> , <q:math xmlns:q="http://www.w3.org/1998/Math/MathML" id="M9"> <q:mi>P</q:mi> <q:mo>=</q:mo> <q:mn>0.54</q:mn> </q:math> ). There was no heterogeneity in the literature ( <s:math xmlns:s="http://www.w3.org/1998/Math/MathML" id="M10"> <s:msup> <s:mrow> <s:mi>χ</s:mi> </s:mrow> <s:mrow> <s:mn>2</s:mn> </s:mrow> </s:msup> <s:mo>=</s:mo> <s:mn>5.23</s:mn> </s:math> , <u:math xmlns:u="http://www.w3.org/1998/Math/MathML" id="M11"> <u:mi>P</u:mi> <u:mo>=</u:mo> <u:mn>0.52</u:mn> </u:math> , <w:math xmlns:w="http://www.w3.org/1998/Math/MathML" id="M12"> <w:msup> <w:mrow> <w:mi>I</w:mi> </w:mrow> <w:mrow> <w:mn>2</w:mn> </w:mrow> </w:msup> <w:mo>=</w:mo> <w:mn>0</w:mn> <w:mi>%</w:mi> </w:math> ), and there was no significant publication bias. Conclusion. There is no significant difference in OS and RFS between TIVA and IA patients during breast cancer resection. The prognostic effects of TIVA and IA were similar.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".