Gleason upgrading with time in a large, active surveillance cohort with long-term follow-up.
Bibliographic record
Abstract
1 Background: Active surveillance (AS) is an accepted management strategy for localized prostate cancer. However, the rate of pathological upgrading has not been well described in mature study cohorts. Furthermore, concern exists over the possibility of prostate cancer dedifferentiation with time in patients on AS. Methods: Patients in our prospectively collected AS database with at least one repeat prostate biopsy were included. Linear regression analysis was used to estimate the proportion of patients upgraded (Gleason 6 to 3+4 or higher, Gleason 3+4 to 4+3 or higher) with time from diagnostic biopsy. Results: 593 of 862 patients in our cohort had at least one repeat biopsy. Median follow-up was 6.4 years (max. 20.2 years). The total number of biopsies ranged from 2 to 6. 20% of patients were intermediate risk, 0.3 % high risk, all others low risk. 31.2% of patients were upgraded during active surveillance. The proportion of patients upgraded increased with time, suggesting prostate cancer dedifferentiation occurred at a rate of 1.0%/year (95%CI -0.12 to 2.16%/year). The estimated rate of increase was 2.5 times higher in patients with intermediate risk disease at diagnosis (rate 1.9%/year, 95%CI -0.7-4.6) compared with those with low risk disease (rate 0.75%/year, 95%CI -0.5-2.0). Further analysis is underway. 62% of upgraded patients (n=114) went on to have active treatment. Patients who were upgraded and treated had significantly greater PSA velocities (median 1.2 ng/ml/y vs 0.42 ng/ml/y, p=0.01) and significantly higher Gleason scores when upgraded, than those who remained on surveillance (21.8% vs 2.8% Gleason 8-10, p<0.01). Conclusions: This is the largest re-biopsy cohort, with long-term follow-up, described to date, enabling the first estimates of prostate cancer dedifferentiation in patients on AS. Dedifferentiation rates appear higher in patients with intermediate risk prostate cancer compared with those who are low risk at baseline.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".