The role of topotecan as second-line therapy in patients with recurrent ovarian cancer.
Bibliographic record
Abstract
INTRODUCTION: Up to 80% of patients with advanced ovarian cancer will recur following first-line platinum containing chemotherapy. Topotecan has recently been used as a second-line agent in treatment of advanced ovarian disease. The aim of the study was to evaluate the effect of topotecan on response rate and progression-free interval on patients with recurrent ovarian cancer who had been treated with platinum-containing first-line chemotherapy. METHODS: A retrospective review of all cases of recurrent ovarian cancer treated with topotecan was done. Response was determined using radiologic reports (CT scans, ultrasound scans), CA-125 level and the clinical evaluation. Response type was determined using World Health Organization (WHO) criteria. RESULTS: Between 1998-2000, a total of 43 patients were treated with topotecan. Median age was 57 (range 41-80), 40/43 patients had stage III and IV, 37/43 patients had Grade 3 tumors. Seventeen of 43 patients (39.5%) demonstrated stable disease and 9/43 (21%) patients demonstrated partial response. Median time to response was eight weeks, median progression-free interval was 31 weeks and median time of follow-up and survival was 48 weeks. CONCLUSION: Topotecan is considered a reasonable option for treatment of patients with recurrent ovarian cancer that have failed previous treatment with platinum-containing chemotherapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".