The percentage of positive biopsy cores in predicting biochemical recurrence and adverse pathology in prostate cancer patients after radical prostatectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Biochemical recurrence (BCR) occurs in more than one-third of prostate cancer (PCa) patients 10 years after radical prostatectomy. The percentage of positive biopsy cores (PPBC) obtained from prostate needle biopsy is suggested as one of the predictors of BCR. We aim to investigate the role of PPBC in predicting BCR and adverse pathology in PCa patients after RP. Methods: A systematic search was conducted based on PRISMA guidelines from Pubmed, Scopus, and Cochrane Library databases up to July 2022. We screened studies that met our inclusion criteria and NOS (Newcastle-Ottawa Scale) was utilized as the quality assessment tool. The primary outcome was BCR measured as Hazard Ratios (HRs). The secondary outcome was adverse pathology, including positive surgical margin (PSM), Extra-prostatic disease (EPD), and seminal vesicle invasion (SVI). Review Manager®5.4 was used as the statistical analysis tool. Results: A total of 5971 patients were included from eleven eligible studies with overall good quality scores. Eleven studies were included in the qualitative synthesis and five of them were analyzed in the meta-analysis. The pooled analysis demonstrated that higher PPBC has a 2.77 times risk of BCR (OR 2.77 (95% CI: 1.97, 3.9; p<0.00001) after RP. Similarly, it has significant results in SVI (OR 2.61 (95% CI: 1.19, 5.73; p=0.02). However, there were insignificant results in terms of EPD (p=0.17) and PSM (p=0.33). Conclusion: This systematic review and meta-analysis (SRMA) indicate that a high PPBC is strongly correlated with a greater risk of BCR and SVI, but not EPD and PSM in patients following RP.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".