Volume-related sequence of tumor distribution pattern in prostate carcinoma: importance of posterior midline crossover in predicting tumor volume, extracapsular extension, and seminal vesicle invasion.
Bibliographic record
Abstract
AIM: To evaluate intraprostatic distribution of prostate carcinoma as a function of increasing tumor size and its potential clinical relevance. METHODS: Forty-six prostates with different tumor extent were three dimensionally reconstructed and analyzed with emphasis on number of separate tumors (multifocality) and its distribution on both sides of the urethral midline (laterality). RESULTS: Three tumor distribution patterns were identified: multiple bilateral without posterior midline crossover, multiple bilateral with crossover, and single bilateral (global) tumors. Unilateral tumors were rare (2%). The pattern of tumor distribution was associated with total tumor volume, presence and volume of high grade component, presence of extracapsular extension, and seminal vesicle involvement. Bilateral tumors with crossover were larger than bilateral tumors without crossover (Spearman's rho=0,728, P<0.001) and were associated with adverse pathological features including capsular penetration, seminal vesicle invasion, and surgical margin involvement. However, only high-grade volume was independently and highly associated with seminal vesicle involvement (OR=2.64, 95%, CI=1.181-5.340, P<0.001). Total (OR=2.53 [1.23-3.74], P<0.001) and index tumor (OR=2.54 [1.31-4.93], P<0.001) volumes were independently associated with capsular penetration. CONCLUSIONS: The distribution of bilateral prostatic carcinomas with and without crossover may have clinical relevance because of their relation to total and high-grade volume.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".