Dissecting the Evolving Risk of Relapse over Time in Surveillance for Testicular Cancer
Bibliographic record
Abstract
Testicular cancer is the most common malignancy in young men, and the incidence is increasing in most countries worldwide. The vast majority of patients present with clinical stage I disease, and surveillance is being increasingly adopted as the preferred management strategy. At the time of diagnosis, patients on surveillance are often counselled about their risk of relapse based on risk factors present at diagnosis, but this risk estimate becomes less informative in patients that have survived a period of time without experiencing relapse. Conditional survival estimates, on the other hand, provide information on a patient's evolving risk of relapse over time. In this review, we describe the concept of conditional survival and its applications for surveillance of clinical stage I seminoma and nonseminoma germ cell tumours. These estimates can be used to tailor surveillance protocols based on future risk of relapse within risk subgroups of seminoma and nonseminoma, which may reduce the burden of follow-up for some patients, physicians, and the health care system. Furthermore, conditional survival estimates provide patients with a meaningful, evolving risk estimate and may be helpful to reassure patients and reduce potential anxiety of being on 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".