Comparison Study Between Laparoscopic Radical Prostatectomy versus Robotic Radical Prostatectomy in Patient with TUR-P History
Bibliographic record
Abstract
Background: The number of men with benign prostate hyperplasia undergoing transurethral resection of prostate (TURP) with the subsequent development of prostate cancer has been increasing. This study aimed to compare the surgical, oncological, and functional outcomes of robotic and laparoscopic radical prostatectomy techniques in patients with the history of TURP.Methods: Literature search of electronic databases was performed through Pubmed, Science Direct, SCOPUS, and CENTRAL databases. Cochrane Risk of Bias Tool was then employed to assess the risk of bias in each study. Grey literature was also searched from sources such as Cancer Care Ontario and conference abstracts. Critical appraisals of included studies were conducted using the Newcastle-Ottawa Scale.Results: The searches located 1258 citations, but only 11 studies were included in the final selection. Most studies had a good methodological quality based on the Ottawa Scale. The mean age of samples was varied among each study from 61.8 to 70.8 years. The TURP history significantly affects biochemical recurrences (OR 2.29, 95% CI 1.14—4.59), intraoperative blood loss (MD 57 ml; 95% CI 6—108 m), prolonged operative duration (MD 20 minutes; 95% CI 3—37 minutes), and surgical complications (OR 2.54, 95% CI 1.79—3.60) following radical prostatectomy for prostate cancer. In the subgroup analysis, only prolonged operative duration and surgical complications were significant both in laparoscopic and robotic radical prostatectomy. There was no association between the TURP history and the positive surgical margin rate in total and subgroup analyses. Conclusions: The previous TURP history affects the outcomes of patients who underwent radical prostatectomy, either laparoscopic or robotic.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".