Patterns of Relapse after Chemotherapy in Patients with High-Risk Non-Seminomatous Germ Cell Tumor
Bibliographic record
Abstract
OBJECTIVES: We investigated the pattern of relapse after chemotherapy in patients with high-risk germ cell tumor (GCT) to critically review common follow-up procedures including close monitoring of serum tumor markers and radiologic procedures. METHODS: 645 patients received first-line (434 patients) or salvage platinum-based (211 patients) high-dose chemotherapy in three multicenter trials. Retrospective analysis comprised 77 patients after first-line and 61 after salvage chemotherapy, who had achieved at least a partial remission but progressed afterwards. RESULTS: At relapse, 24% of the patients presented with an isolated elevation in serum tumor markers, 26% with pathologic radiologic confirmation with negative tumor markers, and 42% with elevated tumor markers and radiologically confirmed progression. Relapse was detected by clinical symptoms in 8%. 46% relapsed within 3 months and 97% within 2 years. Relapse pattern did not correlate with tumor marker status or metastasis location prior to chemotherapy, line of chemotherapy, response status after chemotherapy or time point of relapse. CONCLUSION: In high-risk GCT patients, relapse after chemotherapy is detected either by tumor marker elevation alone, radiologic imaging alone or both, in one third each. Close monitoring including serum tumor markers, radiologic imaging and clinical examination appears warranted within the first 2 years.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".