Bibliographic record
Abstract
Introduction and Objectives: While patients are counselled regarding their baseline risk of relapse on active surveillance (AS) for clinical stage I germ cell tumours (CSI GCTs), it would be beneficial to update their relapse risk estimate once patients have survived a period of time without relapse -a concept known as conditional survival.This has not been determined for CSI GCTs.Methods: We performed a retrospective review of 1239 patients with CSI GCTs managed with AS.Conditional relapse-free survival (cRFS) estimates were calculated by using the multiplicative law of probability at time periods coinciding with our AS followup protocol.We stratified patients according to validated risk factors for relapse.We used linear regression to determine cRFS trends over time.Models were validated using discrimination and calibration.Results: Disease relapse occurred in 126 (27%) of the 464 patients with CSI non-seminoma GCT (NSGCT).During the first five years, cRFS estimates increased from 58% to 100% in NSGCT patients with CSIB disease and pure embryonal carcinoma in orchiectomy pathology.Disease relapse occurred in 135 (17%) of the 775 patients with CSI seminoma.During the first five years, cRFS estimates increased from 80% to 97% for seminoma patients with tumour size ≥3cm.Over time, cRFS increased significantly (p<0.02) in all models stratified by risk factors for relapse in both NSGCT and seminoma.All models demonstrated good discrimination and calibration.Conclusions: cRFS increases rapidly over time in patients managed with AS for CSI seminoma and NSGCT.This information, when provided to patients, may aid in patient counselling and decrease anxiety; moreover, it could be used to adjust followup schedules to minimize radiation exposure and cost of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.245 | 0.078 |
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".