Low Other Cause Mortality Rates Reflect Good Patient Selection in Patients with Prostate Cancer Treated with Radical Prostatectomy
Bibliographic record
Abstract
PURPOSE: Treatment decisions in patients with prostate cancer are affected by patient age regardless of higher life expectancy compared to the baseline population. Our aim was to quantify cancer specific and other cause mortality rates after radical prostatectomy. MATERIALS AND METHODS: A total of 8,741 patients with prostate cancer underwent radical prostatectomy between 1992 and 2009 at a European center. Ten-year other cause and cancer specific mortality rates were determined by age and comorbidities, and age and Cancer of the Prostate Risk Assessment Post-Surgical (CAPRA-S) risk groups. Competing risk regression was used for risk factor analyses including clinical and pathological variables. RESULTS: Ten-year other cause mortality rates increased with patient age, including 4.8%, 9.8%, 13.6% and 16.5% in men younger than 60, 60 to 64, 65 to 69 and 70 years or older, respectively. Cancer specific mortality was the leading cause of death in CAPRA-S high risk cases regardless of age. On multivariate analyses age groups achieved independent predictor status for other cause mortality (ages 60 to 64 years HR 1.81, 95% CI 1.26-2.62, 65 to 69 years HR 2.48, 95% CI 1.73-3.56 and 70 years or greater HR 3.02, 95% CI 1.97-4.62) as well as Charlson comorbidity indexes 1 (HR 1.45, 95% CI 1.00-2.09) and 3 or greater (HR 3.99, 95% CI 1.57-10.1). Gleason score 3 + 4 and 4 + 3 or greater, pT3b stage, lymph node invasion and positive margin status achieved independent predictor status when the end point was cancer specific mortality. The CAPRA-S high risk constellation increased cancer specific mortality risk in multifold fashion (HR 26, 95% CI 16-56). CONCLUSIONS: In patients with the CAPRA-S high risk constellation the rate of cancer specific mortality increased in multifold fashion and contributed to most deaths regardless of patient age. Low other cause mortality rates in all age groups showed reasonable patient selection.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".