A nomogram predicting long‐term biochemical recurrence after radical prostatectomy
Bibliographic record
Abstract
BACKGROUND: Men who undergo radical prostatectomy (RP) are at long-term risk of biochemical recurrence (BCR). In this report, the authors have described a model capable of predicting BCR up to at least 15 years after RP that can adjust predictions according to the disease-free interval. METHODS: Cox regression was used to model the probability of BCR (a prostate-specific antigen level>0.1 ng/mL and rising) in 601 men who underwent RP with a median follow-up of 11.4 years. The statistical significance of nomogram predictors was confirmed with a competing-risks regression model. The model was validated internally with 200 bootstraps and externally at 5 years, 10 years, and 15 years in 2 independent cohorts of 2963 and 3178 contemporary RP patients from 2 institutions. RESULTS: The 5-year, 10-year, 15-year, and 20-year actuarial rates of BCR-free survival were 84.8%, 71.2%, 61.1%, and 58.6%, respectively. Pathologic stage, surgical margin status, pathologic Gleason sum, type of RP, and adjuvant radiotherapy represented independent predictors of BCR in both Cox and competing-risks regression models and constituted the nomogram predictor variables. In internal validation, the nomogram accuracy was 79.3%, 77.2%, 79.7%, and 80.6% at 5 years, 10 years, 15 years, and 20 years, respectively, after RP. In external validation, the nomogram was 77.4% accurate at 5 years in the first cohort and 77.9%, 79.4%, and 86.3% accurate at 5 years, 10 years, and 15 years, respectively, in the second cohort. CONCLUSIONS: Patients who undergo RP remain at risk of BCR beyond 10 years after RP. The nomogram described in this report distinguishes itself from other tools by its ability to accurately predict the conditional probability of BCR up to at least 15 years after surgery.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".