Length of positive surgical margins after radical prostatectomy: Does size matter? – A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objectives The prognostic capacity of positive surgical margins (PSM) for biochemical recurrence (BCR) is unclear, with inconsistent findings across published studies. We aimed to systematically review and perform a meta-analysis exploring the impact of Positive surgical margin length on biochemical recurrence in men after radical prostatectomy. Methods A search was conducted using the MEDLINE, Scopus, Embase and Cochrane databases according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. The quality of the studies was assessed using the Newcastle-Ottawa scale, and the protocol was registered in advance (PROSPERO: CRD42020195908). This meta-analysis included 16 studies with BCR as the primary outcome measure. Results Studies used various dichotomised thresholds for PSM length. A subgroup meta-analysis was performed using the reported multivariable hazard ratio (Continuous, 3, and 1 mm PSM length). PSM length (continuous) was independently associated with an increased risk of BCR (7 studies, HR 1.04 (CI 1.02–1.05), I 2 = 8% p < 0.05). PSM length greater than 3 mm conferred a higher risk of BCR compared to less than 3 mm (4 studies, HR 1.99 (1.54–2.58) I 2 = 0%, p < 0.05). There was also an increased risk of BCR associated with PSM length of less than 1 mm compared to negative surgical margins (3 studies, HR 1.46 (1.05–2.04), I 2 = 0%, P = 0.02). Conclusion PSM length is independently prognostic for BCR after radical prostatectomy. Further long-term studies are needed to estimate the impact on systemic progression.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.051 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".