Incidental Prostate Adenocarcinoma in Cystoprostatectomy Specimens: Partial Versus Complete Prostate Sampling
Bibliographic record
Abstract
BACKGROUND: The rate of incidental prostate adenocarcinoma (PCa) detection in radical cystoprostatectomy (RCP) varies widely, ranging from 15% to 54%. Such variability may be explained by institutional differences in prostate grossing protocols. Either partial or complete submission of the prostate gland in RCP may result in detection of clinically insignificant or significant incidental PCa. The aim of the study was to compare the clinical significance of PCa in RCP specimens in partial versus complete sampling. MATERIAL: Seventy-two out of 158 RCP cases showed incidental PCa. The pathologic features, including Gleason score, margin status, extraprostatic extension (EPE), seminal vesicle invasion (SVI), PCa stage, and tumor volume, were assessed. RESULTS: The 72 cases were divided into partial (n = 21, 29.1%) and complete sampling (n = 51, 70.8%) groups. EPE was detected in 13/72 (18.1%) with 11/13 (84.6%) cases in the complete group. Positive margins were present in 11/72 (15.3%) with 9/11 (81.8%) in the complete group. SVI was detected in 4/72 (5.6%) with 3/4 (75.0%) in the complete group. Overall, 4/72 (5.6%) had a Gleason score >7, all of which were in the complete group. CONCLUSION: Our data suggest that complete sampling of the prostate may be the ideal approach to grossing RCP specimens, allowing for greater detection of clinically significant incidental PCa.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".