Comparative Analysis of Holmium Laser Enucleation of the Prostate (HoLEP) and Robotic-Assisted Simple Prostatectomy (RASP) in BPH Management: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: As the prevalence of benign prostatic hyperplasia (BPH) increases, the demand for surgical interventions that optimize patient outcomes while minimizing complications grows. This systematic review compares the efficacy, efficiency, and safety of holmium laser enucleation of the prostate (HoLEP) with robotic-assisted simple prostatectomy (RASP), providing insights for evidence-based surgical decision-making in BPH treatment. MATERIALS AND METHODS: Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the study protocol was registered with Prospero (CRD42024509627). Searches were conducted in Medline, Embase, Web of Science, Scopus, and Cumulative Index to Nursing and Allied Health Literature up to February 1, 2024, to include studies that compare HoLEP and RASP in patients with BPH. Risk of bias was evaluated using the Newcastle Ottawa Scale. RESULTS: HoLEP and RASP demonstrated equivalent effectiveness in treating BPH, as shown by similar functional outcomes such as maximum urinary flow rate and postvoid residual volume. However, HoLEP outperformed RASP in several operational efficiency metrics, reducing operative time by 49.48 minutes, hospitalization duration by 1.5 days, and catheterization period by 3.8 days. HoLEP also significantly reduced the risk of blood transfusions by 75%. Patients undergoing RASP were 1.87 times more at risk for grade 2 complications and 3.41 times more at risk for developing grade 3 or above complications. CONCLUSIONS: HoLEP and RASP are effective for managing BPH. HoLEP shows advantages in recovery metrics and lower blood transfusion rates, while RASP benefits from ease of implementation in robotic-equipped facilities. Optimizing surgical outcomes will depend on reducing disparities in technique adoption, improving surgical training, and aligning with evidence-based guidelines.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".