Assessment of the optimal number of positive biopsy cores to discriminate between cancer‐specific mortality in high‐risk versus very high‐risk prostate cancer patients
Bibliographic record
Abstract
BACKGROUND: Number of positive prostate biopsy cores represents a key determinant between high versus very high-risk prostate cancer (PCa). We performed a critical appraisal of the association between the number of positive prostate biopsy cores and CSM in high versus very high-risk PCa. METHODS: Within Surveillance, Epidemiology, and End Results database (2010-2016), 13,836 high versus 20,359 very high-risk PCa patients were identified. Discrimination according to 11 different positive prostate biopsy core cut-offs (≥2-≥12) were tested in Kaplan-Meier, cumulative incidence, and multivariable Cox and competing risks regression models. RESULTS: Among 11 tested positive prostate biopsy core cut-offs, more than or equal to 8 (high-risk vs. very high-risk: n = 18,986 vs. n = 15,209, median prostate-specific antigen [PSA]: 10.6 vs. 16.8 ng/ml, <.001) yielded optimal discrimination and was closely followed by the established more than or equal to 5 cut-off (high-risk vs. very high-risk: n = 13,836 vs. n = 20,359, median PSA: 16.5 vs. 11.1 ng/ml, p < .001). Stratification according to more than or equal to 8 positive prostate biopsy cores resulted in CSM rates of 4.1 versus 14.2% (delta: 10.1%, multivariable hazard ratio: 2.2, p < .001) and stratification according to more than or equal to 5 positive prostate biopsy cores with CSM rates of 3.7 versus 11.9% (delta: 8.2%, multivariable hazard ratio: 2.0, p < .001) in respectively high versus very high-risk PCa. CONCLUSIONS: The more than or equal to 8 positive prostate biopsy cores cutoff yielded optimal results. It was very closely followed by more than or equal to 5 positive prostate biopsy cores. In consequence, virtually the same endorsement may be made for either cutoff. However, more than or equal to 5 positive prostate biopsy cores cutoff, based on its existing wide implementation, might represent the optimal choice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".