Intermediate clinical endpoints: A bridge between progression‐free survival and overall survival in ovarian cancer trials
Bibliographic record
Abstract
Ovarian cancer patients are usually diagnosed at an advanced stage, experience recurrence after platinum-based chemotherapy, and eventually develop resistance to chemotherapy. Overall survival (OS), which has improved in recent years as more active treatments have been incorporated into patient care, is regarded as the most clinically relevant endpoint in ovarian cancer trials. However, although there remains a significant need for new treatments that prolong OS further without compromising quality of life, it has become increasingly difficult to detect an OS benefit for investigational treatments because of the use of multiple lines of chemotherapy to treat ovarian cancer. Progression-free survival (PFS), which measures the time to disease progression or death, is unaffected by postprogression therapies but does not evaluate the long-term impact of investigational treatments on tumor biology and responses to future therapies. Recent clinical trials of targeted agents in relapsed ovarian cancer have shown improvements in PFS but not OS, and this is possibly reflective of the long postprogression survival (PPS) period associated with this disease. Intermediate endpoints such as the time to second disease progression or death and the time to second subsequent therapy or death may provide supportive evidence for clinically meaningful PFS improvements and may be used to determine whether these improvements persist beyond the first disease progression and throughout subsequent lines of therapy. For clinical trials that have settings with a long PPS duration and/or involve multiple rounds of postprogression therapy, a primary endpoint of PFS supported by intermediate clinical endpoints and OS may provide a more comprehensive approach for evaluating efficacy.
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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 | 0.101 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".