Conditional risk of relapse in patients with germ cell testicular tumors
Bibliographic record
Abstract
PURPOSE OF REVIEW: Germ cell testicular tumors (GCTTs) are the most common malignancy in young men, and the incidence is increasing worldwide. Most patients present with clinical stage I (CS1) disease, and active surveillance is being increasingly adopted as the preferred initial treatment modality. In this review, we describe the concept of conditional risk of relapse (CRR), an evolving risk estimate for CS1 GCTT patients on active surveillance who have not relapsed. RECENT FINDINGS: At diagnosis, patients are often counseled about their initial risk of relapse based on known risk factors present at diagnosis. However, the risk estimate becomes less informative in patients who have survived a period of time without experiencing relapse. CRR, on the other contrary, provides specific information on a patient's evolving risk of relapse over time. This dynamic estimate can be used to tailor surveillance protocols based on future risk of relapse within risk subgroups. SUMMARY: Implementation of CRR in patients on active surveillance can reduce the burden of follow-up, the number of physician visits and tests, and lower costs for the healthcare system. Finally, CRR estimates provide patients with a meaningful, evolving risk estimate, and may help reassure patients and reduce potential anxiety while continuing active surveillance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".