Robotic-assisted radical prostatectomy in a system of universal health care with limited access to high-tech resources.
Bibliographic record
Abstract
116 Background: The goal of radical prostatectomy (RP) is to minimize the positive surgical margin (PSM) rate. Robotic- assisted radical prostatectomy (RALP) has the theoretical advantages of enhanced ability to identify surgical dissection planes to improve surgical outcome but is associated with a learning curve. The objective is to review the PSM rate and intra- and postoperative complications (Clavien grading) correlating with possible risk factors in a low-density RALP by a high-volume, open RP surgeon working in the Canadian Healthcare System. Methods: The data of 56 RALP from April 2004 to Dec 2009 by one surgeon contemporaneously performed 830 RP. PSM was analyzed and correlated with clinical variables. Univariable and multivariable analyses were used. Results: PSM 19 (34%) cases. At univariable analysis, no association was observed between PSM and age BMI, PSA, number positive cores, % positive cores, Gleason biopsy and clinical stage. Pathological Gleason (p=0.012), pathological stage (p= 0.003) and % tumor in surgical specimen (p=<0.001) were associated with increased risk of PSM. At multivariable the % tumor in surgical specimen (p<0.001) were only independent predictive factor for PSM. According to localization of PSM Apical 9 (48%), PLR 4 (21%), PLL 4 (21%) and bladder neck 1 (5%). Mean follow-up 23 months. In PSM, 12 (63%) showed an undetectable PSA (≤0.01ng/mL) at 3 months. Using Clavien grading, 9 surgical complication in 9(16%) patients: 6 (grade 1–2) and 3 (grade 3–4). 4 patients with PSM had complications, 2 (grade1-2) and 2 (grade 3–4). At multivariable analyses, BMI (p=0.008) was the only predictive factor for occurrence of complications. Conclusions: The pathological stage and tumor (%) in the surgical specimen were independent predictive factor for PMS. Applying the Clavien grading, most of the patients presented minor complications. The Canadian Universal Healthcare system with limited access to high-tech resources may not be ideal for introducing an expensive new surgical technique, especially for an ubiquitous procedure such as RP. However, results of our low-density series or sporadic cases were not inferior to reported initial experience series of RALP in the literature. No significant financial relationships to disclose.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".