Efficacy and safety of cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy for epithelial ovarian cancer: a systematic review and updated meta-analysis
Bibliographic record
Abstract
BACKGROUND: The high incidence of primary and recurrent ovarian cancer after surgery imposes a significant economic burden. Cytoreductive Surgery combined with Hyperthermic Intraperitoneal Chemotherapy (CRS + HIPEC) shows promise as a treatment for epithelial ovarian cancer (EOC). This study aims to evaluate CRS + HIPEC's potential to improve survival outcomes, such as overall survival (OS) and progression-free survival (PFS) while reducing adverse events and enhancing cost-effectiveness. METHOD: A literature review was conducted using the PRISMA framework on databases including Scopus, ProQuest, and PubMed, with quality assessment through the Newcastle-Ottawa Scale (NOS) and Risk of Bias (RoB) 2.0. Quantitative analysis employed RevMan 5.4.1 with a pooled randomized effect model using log [hazard ratio]. RESULT: From 15 studies involving 1982 participants, OS analysis showed significantly higher survival in the CRS + HIPEC group (HR = 0.67, p < 0.0004). Although PFS was higher in this group, the result was not statistically significant (HR = 0.86, p = 0.46). Adverse events were more likely in the intervention group compared to control group (OR = 1.81, p < 0.0001). Cost analysis revealed that the Incremental Cost-effectiveness Ratio per Quality-Adjusted Life Year (ICER/QALY) remains below Indonesia's GDP threshold. CONCLUSION: CRS + HIPEC shows potential benefits in EOC management, particularly in OS and PFS improvement, alongside manageable adverse events and favorable cost-effectiveness. However, study design heterogeneity, differences in HIPEC protocols, and variations in patient populations limit the generalization of outcomes. The difference in response to HIPEC between primary and recurrent EOCs still needs further explanation.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.004 | 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".