How Much Tissue Sampling Is Required When Unsuspected Minimal Prostate Carcinoma Is Identified on Transurethral Resection?
Bibliographic record
Abstract
Abstract Context.—When minimal prostate cancer is detected in the initial transurethral resection of the prostate (TURP) sample, it is uncertain how extensively the remaining tissue should be sampled for accurate grading and staging. Objective.—To identify whether additional partial or complete sampling is required to accurately evaluate TURP samples with minimal cancer (stage T1a). Design.—We prospectively examined all TURP samples in our institution during 1 year. All specimens were sampled randomly in 6 cassettes. When minimal cancer was found, we performed additional partial sampling (1 block per 5 g of remaining tissue), followed by complete submission of all remaining tissue. All samples were evaluated separately to identify possible changes in Gleason score and tumor volume. We performed a cost analysis for the additional tissue sampling. Results.—Of 747 TURP samples evaluated on the initial 6 cassettes, 125 (16.7%) contained prostate cancer. Minimal cancer involving less than 5% of sampled tissue was found in the initial submission in 26 (3.5%) patients. Additional partial examination required 3.5 blocks per case (median; range, 1–23), while complete processing required an additional 5.5 blocks per case (median; range, 2–25). Initial Gleason scores and tumor volumes were not changed in any of the studied cases after evaluating the additional partial and complete samples. In our laboratory, we calculated a cost of $4336 per year for the additional sampling of TURPs with minimal cancer ($1681 for partial and $2655 for complete sampling). Conclusions.—When minimal cancer was found in the first 6 cassettes of transurethral resections, additional partial and complete sampling did not change the initial Gleason scores and tumor volumes.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".