Efficacy of alpha-blockers in medical expulsive therapy for ureteral stones: A systematic review and meta-analysis of randomized controlled trials between 2010 and 2025
Bibliographic record
Abstract
Introduction: Alpha-blockers are widely used in medical expulsive therapy (MET) for ureteral stones; however, the current evidence regarding their comparative effectiveness remains inconsistent. We aimed to evaluate the efficacy and safety of different alpha-blockers in facilitating ureteral stone passage and identify factors influencing treatment outcomes. Methods: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) published between 2010 and 2025. We searched multiple databases for studies comparing alpha-blockers with control interventions or other alpha-blockers for ureteral stones ≤10 mm. Primary outcome was stone expulsion rate; secondary outcomes included time to expulsion, pain episodes, analgesic use, and adverse events. We performed subgroup analyses by alpha-blocker type, stone size, location, and treatment duration. Network meta-analysis assessed comparative effectiveness between agents. Results: Twenty-nine RCTs with a total of 4,256 patients were included. Alpha-blockers significantly increased stone expulsion rates compared to controls (70.9% vs. 56.5%; RR 1.25, 95% CI 1.20-1.32; Number Needed to Treat (NNT) = 7) and reduced expulsion time by approximately three-days. Efficacy was greatest for distal ureteral stones (RR 1.52; Number Needed to Treat (NNT) = 4) and stones 5-10 mm (RR 1.35; NNT = 6). Network meta-analysis revealed efficacy ranking favoring at first terazosin, followed by doxazosin then, silodosin then, tamsulosin then, alfuzosin and last the least effective was naftopidil. Alpha-blockers significantly reduced pain episodes and analgesic requirements. Adverse events were infrequent (Number Needed to Harm (NNH) = 38), with retrograde ejaculation being most common with silodosin. Conclusion: Alpha-blockers significantly improve the stone expulsion rates and reduce expulsion time, especially for distal ureteral stones 5-10 mm in size. While tamsulosin remains the most studied agent, our network meta-analysis suggests terazosin and doxazosin may offer superior efficacy. The favorable risk-benefit profile supports routine use of alpha-blockers for appropriately selected patients with ureteral stones.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| 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".