High-Dose Chemotherapy and Autologous Stem-Cell Transplantation for Ovarian Cancer: An Autologous Blood and Marrow Transplant Registry Report
Bibliographic record
Abstract
BACKGROUND: Autologous transplantation is increasingly used to treat epithelial ovarian cancer. However, it is not clear which patients may benefit. OBJECTIVE: To determine overall and progression-free survival and factors associated with favorable outcome after autotransplantation for ovarian cancer. DESIGN: Observational cohort study. SETTING: 57 centers reporting to the Autologous Blood and Marrow Transplant Registry (ABMTR). PATIENTS: 421 women who received transplants between 1989 and 1996. INTERVENTIONS: High-dose chemotherapy using diverse regimens with hematopoietic stem-cell rescue. MEASUREMENTS: Primary outcomes were progression-free survival and overall survival. Multivariate analyses using Cox proportional hazards regression considered the following factors: age, Karnofsky performance score, initial stage, histologic characteristics, previous therapy, remission status, extent of disease, graft source, transplant regimen, and year of transplantation. RESULTS: Most patients had extensive previous chemotherapy. Forty-one percent had platinum-resistant tumors, and 38% had tumors at least 1 cm in diameter. Only 34 patients (8%) received transplants as part of initial therapy. The probability of death within 100 days was 11% (95% CI, 8% to 14%). Two-year progression-free survival was 12% (CI, 9% to 16%), and 2-year overall survival was 35% (CI, 30% to 41%). Younger age, Karnofsky performance score of at least 90%, non-clear-cell disease, remission at transplantation, and platinum sensitivity were associated with better outcomes. Progression-free and overall survival were 22% (CI, 12% to 33%) and 55% (CI, 42% to 66%), respectively, for women with a high Karnofsky performance score and non-clear-cell, platinum-sensitive tumors. CONCLUSIONS: Some subgroups of patients with ovarian cancer seem to have good outcomes after autotransplantation, although several biases may have affected these observations. Phase III trials are needed to compare such outcomes with outcomes of conventional 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".