A Nomogram Predicting Prostate Cancer-Specific Mortality after Radical Prostatectomy
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it