Prostate cancer mortality and metastasis under different biopsy frequencies in North American active surveillance cohorts
Bibliographic record
Abstract
BACKGROUND: Active surveillance (AS) is an accepted means of managing low-risk prostate cancer. Because of the rarity of downstream events, data from existing AS cohorts cannot yet address how differences in surveillance intensity affect metastasis and mortality. This study projected the comparative benefits of different AS schedules in men diagnosed with prostate cancer who had Gleason score (GS) ≤6 disease and risk profiles similar to those in North American AS cohorts. METHODS: Times of GS upgrading were simulated based on AS data from the University of Toronto, Johns Hopkins University, the University of California at San Francisco, and the Canary Pass Active Surveillance Cohort. Times to metastasis and prostate cancer death, informed by models from the Scandinavian Prostate Cancer Group 4 trial, were projected under biopsy surveillance schedules ranging from watchful waiting to annual biopsies. Outcomes included the risk of metastasis, the risk of death, remaining life-years (LYs), and quality-adjusted LYs. RESULTS: Compared with watchful waiting, AS biopsies reduced the risk of prostate cancer metastasis and prostate cancer death at 20 years by 1.4% to 3.3% and 1.0% to 2.4%, respectively; and 5-year biopsies reduced the risk of metastasis and prostate cancer death by 1.0% to 2.4% and 0.6% to 1.6%, respectively. There was little difference between annual and 5-year biopsy schedules in terms of LYs (range of differences, 0.04-0.16 LYs) and quality-adjusted LYs (range of differences, -0.02 to 0.09 quality-adjusted LYs). CONCLUSIONS: Among men diagnosed with GS ≤6 prostate cancer, obtaining a biopsy every 3 or 4 years appears to be an acceptable alternative to more frequent biopsies. Reducing surveillance intensity for those who have a low risk of progression reduces the number of biopsies while preserving the benefit of more frequent schedules.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".