Salvage chemotherapy after failure of first-line chemotherapy in patients with metastatic testicular cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Because of the small number of relapsed patients, their prognostic variability and the complexity of second-line therapy, randomized studies are largely lacking and treatment recommendations for patients with relapse after first-line chemotherapy are derived from retrospective series or phase II studies. This review summarizes the existing evidence including several recently published larger studies on the use of high-dose chemotherapy in these patients. RECENT FINDINGS: Patients with unfavorable features such as incomplete response to first-line therapy, cisplatin refractoriness, multiple relapses or advanced stage at initial diagnosis have been shown to benefit from salvage high-dose chemotherapy with autologous stem cell support. Long-term survival rates of up to 60% have been reported after salvage high-dose chemotherapy for these patients. The treatment for patients relapsing after complete remission to first-line therapy, cisplatin-sensitive disease and gonadal primary remains controversial. Excellent long-term event-free survival rates of up to 80% have been reported after both conventional and high-dose chemotherapy. Surgery remains an important part of any salvage strategy. SUMMARY: The prognosis of patients relapsing after first-line cisplatin-based chemotherapy has improved with multimodality therapy including conventional and high-dose chemotherapy, surgery and radiation. The treatment of these patients requires a close cooperation of experienced medical oncologists, urologists and radiation oncologists.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".