Parameters predicting postoperative unilateral disease in patients with unilateral prostate cancer in diagnostic biopsy: a rationale for selecting hemiablative focal therapy candidates
Bibliographic record
Abstract
BACKGROUND: Focal hemiablative therapy for prostate cancer is a new treatment alternative. Unilateral and unifocal disease are its main limitations. The aim of this study was to identify the epidemiological, clinical and pathological parameters that may predict unilateral disease in patients diagnosed with prostate cancer. METHODS: We performed a retrospective analysis of patients at our institution between January 2005 and January 2011. Only patients with unilateral disease in prostate biopsy were part of the study. The analysis included age, preoperative prostate-specific antigen (PSA) and its density, prostate volume, biopsy first and second Gleason pattern and Gleason summary, number of biopsy cores, percentage of cancer in biopsy material and the presence of high-grade prostatic intraepithelial neoplasia. Their role as potential predictors was evaluated by univariate and multivariate analysis. RESULTS: A total of 161 patients had unilateral disease after prostate biopsy. A significant correlation was found between prostate volume, PSA density and percentage of cancer in biopsy material and the presence of unilateral disease in the surgical specimen. These are the same factors significant in the univariate analysis. The results of the multivariate analysis demonstrated that PSA density (p = 0.015) and percentage of cancer in biopsy material (p = 0.028) are the most significant predictors. INTERPRETATION: Our results demonstrate that PSA density and the percentage of cancer in biopsy cores are significant predictors for prostate cancer unilaterality and should be considered for the selection of hemiablative focal therapy candidates.
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".