Palliative systemic therapy for women with recurrent epithelial ovarian cancer: current options
Bibliographic record
Abstract
OBJECTIVES: To review the available systemic treatments for women with recurrent ovarian cancer. METHODS: A literature review was conducted for recurrent ovarian cancer articles in English, including randomized trials, Phase II trials, or reviews. RESULTS: We discuss the efficacy and toxicity outcomes associated with systemic therapy for platinum-sensitive and platinum-resistant ovarian cancer. Clearly, platinum-based combination systemic therapy shows a prolonged progression-free interval compared with single-agent chemotherapy with a low toxicity profile. No clear superior management strategy exists for platinum-resistant/refractory disease. Novel targeted antiangiogenic agents (eg, bevacizumab), angiopoeitin inhibitors (eg, AMG 386), and poly ADP ribose polymerase inhibitors (eg, olaparib) are reviewed. CONCLUSION: Although combination platinum-based chemotherapy has shown benefits for women with platinum-sensitive recurrent ovarian cancer, the optimal treatment strategy for those with platinum-resistant or platinum-refractory disease is not clear. Molecular and genetic targeted therapies may provide opportunities for those women with tumor profiles that show sensitivity for specific agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".