Population-based 10-year event-free survival after radical prostatectomy for patients with prostate cancer in British Columbia
Bibliographic record
Abstract
INTRODUCTION: We determined (1) the 10-year survival outcomes after radical treatment of prostate cancer and (2) the 10-year event-free survival following radical prostatectomy (RP) at a population-level in British Columbia (BC), Canada. METHODS: We identified all men with a new diagnosis of prostate cancer in BC between 1999 and 2000. Those treated with RP, external beam radiotherapy (EBRT) or brachytherapy (BT) were identified. Overall survival, and prostate cancer specific survival (PCSS) were calculated from diagnosis using the Kaplan-Meier method. For those men treated with RP, we calculated the 10-year event-free survival (freedom from salvage EBRT or androgen ablation, or death from prostate cancer). Reasons for initiating androgen therapy were unknown and may include symptomatic metastatic disease or asymptomatic biochemical recurrence. An important limitation was the absence of prostate-specific antigen data for staging or follow-up. RESULTS: Among 6028 incident cases, RP was the curative-intent treatment within 1 year in 1360 (22.6%) patients, EBRT in 1367 (22.7%), and BT in 357 (5.9%). The 10-year PCSS was 98% for RP, 95% for EBRT and 98% for BT (log rank p < 0.0001). The 10-year overall survival was 87%. The 10-year event-free survival for those treated with RP was 79% and varied with Gleason grade: 87%, 74%, and 52% for Gleason 2-6, 7, and 8-10, respectively (p < 0.0001). CONCLUSIONS: This population-based study provides outcomes which can inform patient decision-making and provide a benchmark to which other therapies can be compared. Event-free rates for patients treated with RP vary with Gleason score. There is room for improvement in the outcomes of patients with high Gleason score treated with RP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".