High-dose chemotherapy with autologous stem-cell transplantation for relapsed metastatic germ cell tumors
Bibliographic record
Abstract
INTRODUCTION: High-dose chemotherapy with autologous stem-cell transplantation (HDC-ASCT) is standard therapy for metastatic germ cell tumors (mGCTs) in patients whose disease progresses during or after conventional chemotherapy. We conducted a retrospective review of HDC-ASCT in relapsed mGCT patients in the province of Alberta, Canada, over the past two decades. METHODS: Patients with mGCTs who received HDC-ASCT at two provincial cancer referral centers from 2000-2018 were identified from institutional databases. Baseline clinical and treatment characteristics were collected, as well as overall survival (OS ) and disease-free survival (DFS). Relevant prognostic variables were analyzed. RESULTS: Forty-three patients were identified. The median age was 28 years (range 19-56). A majority (95%) had non-seminoma histology and testis/retroperitoneal primary (84%). Twenty patients (47%) had poor-risk disease, as per The International Germ Cell Consensus Classification (IGCCC), at start of first-line chemotherapy. HDC-ASCT was used as second-line therapy in 65% of patients, and 58% of ASCT patients received tandem transplants. Median followup after ASCT was 22 months (range 2-181). At last followup, 42% of patients were alive without disease, including 3/7 (43%) of patients with primary mediastinal disease. Two-year and five-year DFS/OS ratios were 44%/65% and 38%/45%, respectively. Median OS and DFS for all patients were 30.0 months (13.3-46.6) and 8.0 months (0.9-15.1), respectively. CONCLUSIONS: We found that HDC-ASCT is an effective salvage therapy in mGCT, consistent with existing literature. Patients appeared to benefit regardless of primary site. Although limited by small sample size, we found a numerical difference in DFS and OS between second- and third-line HDC-ASCT and single vs. tandem ASCT.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".