Shifting Survival Horizons in Advanced Ovarian Cancer: A Conditional Survival Perspective
Bibliographic record
Abstract
Advanced-stage epithelial ovarian cancer (EOC) is defined by biological heterogeneity and poor outcomes, and traditional survival metrics fail to reflect the evolving nature of prognosis as patients survive longer. This study aimed to evaluate conditional survival (CS) in advanced EOC using both overall survival (OS) and progression-free survival (PFS) metrics to provide a dynamic understanding of long-term outcomes. We retrospectively analyzed 808 patients with FIGO stage III-IV EOC who underwent surgery at Baskent University Ankara Hospital between 2004 and 2024. CS estimates were calculated for additional 1- and 5-year intervals among patients who had already survived 6 months, 1, 3, or 5 years after surgery. Median OS and PFS were 4.37 and 1.70 years, respectively. Peritoneal dissemination and platinum resistance were independent predictors of poor survival. Approximately 11% of patients achieved survival beyond ten years. The 1-year CS-OS increased from 87% at 6 months to 95% at 5 years, while the 5-year CS-OS rose from 49% to 66%; corresponding CS-PFS values increased from 89% to 95% and from 44% to 62%. Conditional survival analysis underscores that prognosis in advanced ovarian cancer is not static but continually improves with time survived and sustained disease control. These insights redefine long-term outcomes and provide a modern foundation for individualized patient counseling and survivorship planning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".