Phase II Feasibility Study of Sequential Couplets of Cisplatin/Topotecan Followed by Paclitaxel/Cisplatin as Primary Treatment for Advanced Epithelial Ovarian Cancer: A National Cancer Institute of Canada Clinical Trials Group Study
Bibliographic record
Abstract
PURPOSE: Despite the improved results in advanced ovarian cancer achieved with the addition of paclitaxel to frontline therapy, there remains room for improvement. One approach is to add new agents such as topotecan. Because myelosuppression limits the delivery of topotecan with paclitaxel/cisplatin in a three-drug combination, we explored giving sequential couplets of cisplatin/topotecan followed by paclitaxel/cisplatin. PATIENTS AND METHODS: Forty-four patients with residual epithelial ovarian carcinoma after primary surgery were studied. Cisplatin 50 mg/m(2) on day 1 and topotecan 0.75 mg/m(2) on days 1 through 5 were administered at 21-day intervals for four cycles, followed by interval debulking surgery (if optimal debulking was not achieved with primary surgery), and then paclitaxel 135 mg/m(2) over 24 hours on day 1 and cisplatin 75 mg/m(2) on day 2 at 21-day intervals for four cycles. RESULTS: Such sequential couplets are feasible. Myelotoxicity was the major toxic effect, but it was of short duration. The granulocyte nadir with topotecan/cisplatin occurred late (median, day 18), so retreatment on day 21 was not always possible. There was no unexpected nonhematologic toxicity. The regimen was active in this group of patients who had undergone largely suboptimal debulking surgery. In 34 patients with clinically measurable disease, the overall response rate was 78%, and 30 (77%) of the 39 patients with elevated CA 125 levels at baseline had normalization of CA 125 levels by the end of therapy. CONCLUSION: Sequential couplets of cisplatin/topotecan followed by paclitaxel/cisplatin are feasible. The efficacy data in this suboptimal group of patients has encouraged us to proceed with a randomized study based on this approach.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".