Does nerve-sparing radical prostatectomy increase the risk of positive surgical margins and biochemical progression?
Bibliographic record
Abstract
BACKGROUND: Since the introduction of nerve-sparing radical prostatectomy (NSRP), there have been concerns about the increased risks of positive surgical margins (PSM) and biochemical progression (BP). We examined the relationship of NSRP with PSM and BP using a large, mature dataset. MATERIALS AND METHODS: Patients who underwent RP for clinically localized prostate cancer at our center between 1997 and 2008 were identified. Patients who received neoadjuvant therapy were excluded. We examined the relation of NSRP to the rate of PSM and BP in univariate and multivariate analyses adjusting for clinical and pathological variables including age, pretreatment prostate-specific antigen (PSA) levels and doubling time, and pathological stage and grade. RESULTS: In total, 856 patients were included, 70.9% underwent NSRP and 29.1% had non-NSRP. PSM rates were 13.5% in the NSRP group compared to 17.7% in non-NSRP (P=0.11). In a multivariate analysis, non-NSRP was preformed in patients with a higher pathological stage (HR 1.95, 95% CI 1.25-3.04, P=0.003) and a higher baseline PSA level (HR 1.04, 95% CI 1.01-1.08, P=0.005). With a median follow-up of 41 months, BP-free survival was 88% for non-NSRP compared to 92% for the NSRP group (log rank P=0.018); this difference was not significant in a multivariate Cox regression analysis (HR 0.54, 95% CI 0.28-1.06, P=0.09). CONCLUSION: When used in properly selected patients, NSRP does not seem to increase the risk of PSM and disease progression. The most effective way of resolving this issue is through a randomized clinical trial; however, such a trial is not feasible.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".