A Nomogram Predicting Prostate Cancer-Specific Mortality after Radical Prostatectomy
Bibliographic record
Abstract
OBJECTIVE: We describe a model capable of predicting prostate cancer (PCa)-specific mortality up to 20 years after a radical prostatectomy (RP), which can adjust the predictions according to disease-free interval. PATIENTS AND METHODS: 752 patients were treated with RP for organ-confined PCa. Cox regression modeled the probability of PCa-specific mortality. The significance of the predictors was confirmed in competing risks analyses, which account for other-cause mortality. RESULTS: The mean follow-up was 11.4 years. The 5-, 10-, 15- and 20-year actuarial rates of PCa-specific survival were 99.0, 95.5, 90.9 and 85.7%, respectively. RP Gleason sum (p < 0.001), pT stage (p = 0.007), adjuvant radiotherapy (p = 0.03) and age at RP (p = 0.004) represented independent predictors of PCa-specific mortality in the Cox regression model as well as in competing risks regression. Those variables, along with lymph node dissection status (p = 0.4), constituted the nomogram predictors. After 200 bootstrap resamples, the nomogram achieved 82.6, 83.8, 75.0 and 76.3% accuracy in predicting PCa-specific mortality at 5, 10, 15 and 20 years post-RP, respectively. CONCLUSIONS: At 20 years, roughly 20% of men treated with RP may succumb to PCa. The current nomogram helps to identify these individuals. Their follow-up or secondary therapies may be adjusted according to nomogram predictions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".