Bibliographic record
Abstract
Testicular cancer (TC) is the most prevalent tumor in young men aged 15–40 years, with an annual incidence of 3–11 new cases per 100,000 males in Western countries. In 2020, the International Agency for Research on Cancer reported 74,458 newly diagnosed cases of TC globally. The etiology of TC is complex and includes both genetic and environmental factors. The prognosis of TC is excellent with a >90% cure rate and a >95% 5-year survival rate with appropriate treatment. Treatments for TC include active surveillance, chemotherapy, radiotherapy, and retroperitoneal lymph node dissection, depending on the clinical stage and tumor subtype. It is crucial that patients receive information on the diagnosis, therapeutic management options, consequences of treatments, and surveillance protocols, which allows the patient to play an active role in the decision-making process. Fear of recurrence often affects TC survivors. Therefore, it is essential to fully involve the patient in the choice of the treatment to ensure an optimal compliance, especially when selecting the active surveillance strategy. In the modern era, in light of the excellent outcomes achieved in TC management, one of the high priorities is to deliver curative treatments while minimizing long-term toxicity. This focus can have a positive impact on quality of life and life expectancy of TC survivors.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".