Survival benefits of dose-dense early postoperative intraperitoneal chemotherapy in front-line therapy for advanced ovarian cancer: a randomised controlled study
Bibliographic record
Abstract
Dose-dense early postoperative intraperitoneal chemotherapy (DD-EPIC) significantly increased non-progression rate in advanced ovarian cancer (OC) patients. We report final overall survival (OS) results to further strengthen the efficacy of DD-EPIC in the front-line therapy. In this phase 2 trial, 218 patients with FIGO IIIC-IV OC were randomly allocated to receive DD-EPIC followed by intravenous (IV) chemotherapy (DD-EPIC group), or IV chemotherapy alone (IV group). The study was prespecified to detect differences in progression-free survival (PFS) and OS. At a median follow-up period of 69.1 months, the median OS was 67.5 and 46.3 months in the DD-EPIC and IV group, respectively. The probability rate of OS at 5 years was 61.0% with DD-EPIC, and 38.2% with IV (hazard ratio [HR] for death from OC, 0.70; 95% confidence interval [CI], 0.49-1.00). DD-EPIC was associated with a prolonged PFS compared with the IV group (the estimated rate of PFS at 5 years, 26.0% vs. 8.5%; HR for disease progression, 0.64; 95% CI, 0.47-0.86). DD-EPIC was associated with a longer OS than IV chemotherapy alone. It may be considered as a valuable option of the front-line therapy for advanced ovarian cancer.Trial registration: ClinicalTrials.gov, NCT01669226 (date of registration: August 20, 2012).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".