Prevalence and risk factors of contralateral extraprostatic extension in men undergoing radical prostatectomy for unilateral disease at biopsy: A global multi-institutional experience
Bibliographic record
Abstract
INTRODUCTION: We assessed the incidence of contralateral prostate cancer (cPCa), contralateral EPE (cEPE) and contralateral positive surgical margins (cPSM) in patients diagnosed preoperatively with unilateral prostate cancer and evaluated risk factors predictive of contralateral disease extension. METHODS: The occurrence of cPCa, cEPE and cPSM and the side-specific nerve-sparing technique performed were collected postoperatively from 327 men diagnosed with unilateral prostate cancer at biopsy. Parameters, such as the localization, proportion, and percentage of cancer in positive cores, were prospectively collected. RESULTS: Overall, 50.5% of patients had bilateral disease, and were at higher risk when associated with a positive biopsy core at the apex (p = 0.016). The overall incidence of ipsilateral EPE and cEPE were 21.4% and 3.4%, respectively (p < 0.001). Compared to cPCa, ipsilateral disease was at an almost 4-fold higher risk of extending out of the prostate (p < 0.001). None of the criteria tested were identified as useful predictors for cEPE. The low incidence of cEPE in our cohort could limit our ability to detect significance. The overall incidence of ipsilateral PSM and cPSM were 15.3% and 5.8%, respectively (p < 0.001). More aggressive nerve-sparing was not associated with a higher incidence of PSM. Prostate sides selected for more aggressive nerve-sparing were associated with younger patients (p < 0.001), a smaller prostate (p = 0.006), and a lower percentage of cancer in biopsy material (p = 0.008). CONCLUSION: Although the risk of cPCa is high in patients diagnosed with unilateral prostate cancer at biopsy, the risk of cEPE and cPSM is low, yet not insignificant. Contralateral aggressive nerve-sparing should be used with caution and should not compromise oncological outcome.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".