Chemotherapy for recurrent epithelial ovarian cancer previously treated with platinum--a systematic review of the evidence from randomized trials.
Bibliographic record
Abstract
PURPOSE: To evaluate the chemotherapeutic options for women with recurrent epithelial ovarian cancer who have received platinum-based chemotherapy. METHODS: A systematic search of the Medline, CancerLit and Cochrane Library databases was performed for the period from 1984 to June 2001 to find randomized trials comparing second- or higher-line chemotherapy regimens in patients with recurrent platinum-pretreated epithelial ovarian cancer. RESULTS: Seven randomized trials have failed to demonstrate the clear superiority of any one chemotherapy regimen in terms of improvements in long-term survival, quality of life or response rate. One trial detected a statistically significant difference between treatments in progression-free survival, which was longer with cyclophosphamide/doxorubicin/cisplatin than with paclitaxel in women with platinum-sensitive ovarian cancer. Another trial did not show a difference between liposomal doxorubicin and topotecan overall in women with recurrent ovarian cancer but a subgroup analysis detected a significant survival advantage for liposomal doxorubicin over topotecan in women with platinum-sensitive disease. CONCLUSION: The evidence available does not support firm conclusions about the preferred chemotherapy regimen for recurrent ovarian cancer. Randomized trials that compare new drugs with current standard treatments are needed.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".