Head-to-head comparison of Rezum vs Urolift for patient with benign prostatic hyperplasia: A systematic review
Bibliographic record
Abstract
Introduction: Minimally invasive surgical therapies (MISTs) have been developed to treat Benign Prostatic hyperplasia (BPH) while minimizing adverse surgical effect. Most recent developed MIST, Rezum and Urolift, showed effective efficacy in improving, but head-to-head comparison still lacking. This study aims to compare the efficacy and safety of Urolift and Rezum in managing BPH-related LUTS. Methods:A systematic search was conducted in Cochrane, PubMed, EMBASE, SCOPUS and EBSCOhost, identifying observational studies comparing rezum and urolift in BPH patient. Data extraction included demographics, intervention protocols, follow-up duration, and outcomes International Prostate Symptom Score (IPSS) score, IPSS-QoL, sexual function, and reintervention rate. The quality of studies was assessed using the Newcastle-Ottawa Scale. Results:Two cohort with a total of 101 patients with BPH were included. At 12 months, Rezum tends to be superior in improving symptoms severity, while at 2 months of follow-up, Urolift outperformed Rezum. Both intervention preserved patient’s sexual function and does not differ significantly. Reintervention rate significantly higher in Urolift compared to Rezum group. Conclusion: Rezum offers a more effective alternative to Urolift for managing BPH in long term setting, with lower reintervention rate and preserved sexual function. In early setting, Urolift showed higher efficacy and rapid improvement compared to Rezum.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".