Association of absolute amount of pattern 4 disease on prostate biopsy with oncologic outcomes in intermediate-risk prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Managing intermediate-risk prostate cancer is challenging due to the heterogeneity in patient outcomes within this risk category. Evaluating the absolute amount of Gleason pattern 4 disease (GP4) at biopsy using the total linear length of pattern 4 (GP4-TL) or absolute percentage of pattern 4 (APP4) may enhance risk stratification. This review aimed to determine if these absolute measures predict oncologic outcomes in IRPC and to identify optimal prognostic thresholds. METHODS: A systematic review was conducted following PRISMA guidelines. Studies included were those reporting the absolute amount of GP4 on biopsy and related outcomes in IRPC patients undergoing surgery or radiotherapy. Outcomes included biochemical recurrence, androgen deprivation therapy (ADT)-free survival, distant metastasis, prostate cancer-specific mortality, all-cause mortality, and adverse pathology. RESULTS: Seven studies with a total of 2523 patients were included. Analysis revealed that APP4 thresholds were highly predictive of biochemical recurrence, ADT-free survival, and distant metastasis. Both APP4 and GP4-TL were superior to relative % GP4 and Gleason grading (4+3 vs. 3+4) in predicting disease progression and mortality. CONCLUSIONS: The absolute amount of GP4 shows consistent associations with important clinical outcomes and offers an accessible and established method to enhance risk stratification. Further research is needed to define optimal thresholds to guide treatment decisions.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.002 | 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".