Investigational targeted therapies for the treatment of testicular germ cell tumors
Bibliographic record
Abstract
INTRODUCTION: Germ cell tumors (GCTs) are the most common malignancy among men aged between 15 to 45. Despite high cure rates of >90% over all GCTs, 3 to 5% of patients will still die of platinum-refractory disease. New systemic treatment options are needed to improve treatment success in this challenging setting. AREAS COVERED: To review targeted treatment options and preclinical developments in platinum-refractory GCTs, a comprehensive literature search of PubMed, Medline and scientific meeting abstracts on published clinical trials and reports on molecularly targeted approaches was conducted. Outcomes of platinum-refractory disease and of patients failing high-dose chemotherapy remain poor. Currently, no molecularly targeted treatment has shown clinically meaningful activity in unselected patient populations in clinical trials, but individual patients may achieve short-lived objective responses by treatment with sunitinib, brentuximab vedotin or imatinib. Targeted trials based on molecular selection of patients have not yet been performed. EXPERT OPINION: The limited activity of targeted agents in refractory GCT is disappointing. Assessment of druggable biomarkers and marker-stratified treatment may help individual patients, but is largely lacking. The low incidence and high curability of GCTs make the design of larger clinical trials difficult. The potential of novel agents, i.e. immune-checkpoint inhibitors, remains to be elucidated.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".