Paclitaxel as HIPEC-Drug after Surgical Cytoreduction for Ovarian Peritoneal Metastases: A Randomized Phase III Clinical Trial (HIPECOVA)
Bibliographic record
Abstract
Multidisciplinary strategies have transformed the management of advanced ovarian cancer. We aimed to evaluate the effectiveness of paclitaxel in hyperthermic intraperitoneal chemotherapy (HIPEC) following surgical cytoreduction for ovarian peritoneal metastases in a randomized phase III trial conducted between August 2012 and December 2019. Seventy-six patients were randomized to either the HIPEC or no HIPEC group. Although median values for the primary endpoints (recurrence-free survival (RFS) and overall survival (OS)) revealed superior outcomes for the HIPEC (RFS: 23 months, OS: 48 months) over the control group (RFS: 19 months, OS: 46 months), these differences were not statistically significant (p = 0.22 and p = 0.579). Notably, the HIPEC group demonstrated significantly higher 5-year OS and 3-year RFS rates (47.2% and 47.5%) compared to patients without HIPEC (34.5% and 21.3%). Stratification according to Peritoneal Surface Disease Severity Score (PSDSS) showed improved OS and RFS for patients with lower PSDSS (I–II) in the HIPEC-treated group (p = 0.033 and p = 0.042, respectively). The Clavien–Dindo classification of adverse event grades revealed no significant differences between HIPEC and controls (p = 0.482). While overall results were not statistically significant, our long-term follow-up emphasized the potential benefit of HIPEC-associated cytoreduction with paclitaxel, particularly in selected ovarian cancer patients with lower PSDSS indices.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".