Prognostic Stratification of pN1 Prostate Cancer After Radical Prostatectomy: A Competing Risk Analysis from a Multi-institutional Cohort
Bibliographic record
Abstract
Background and objective: Lymph node-positive (pN1) prostate cancer (PCa) is a heterogeneous disease, and a clear definition of prognostic groups is urgently needed. We aimed to assess cancer-related mortality (CRM) in different prognostic groups of pN1 patients, created based on the pathological PCa characteristics and number of positive lymph nodes (LN+). Methods: We conducted a retrospective, multicentre cohort study including 894 patients with pN1 disease treated at 15 European high-volume centres. Independent predictors for CRM were identified and pooled. A prognostic model was constructed for the prediction of CRM, accounting for death from other causes as a competing risk. The 10-yr cumulative risk of mortality was assessed. Key findings and limitations: < 0.005). Conclusions and clinical implications: The pN1 patient population is extremely heterogeneous, with an increased risk of death from PCa rather than death from other causes. In this group of patients, primary cancer characteristics (pT stage, number of LN+, and SM status) still represent the driving factors of CRM. Patient summary: Men with positive lymph nodes on pathology have an increased risk of dying from prostate cancer, rather than from other causes. Our proposed model stratifies patients into groups with different cancer-related prognosis and may aid in personalised clinical decision-making in a postoperative setting.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".