Which combination of medical expulsive therapy is more effective for treatment of distal ureteral stone in adults? A systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Medical Expulsive Therapy (MET) has been recommended as an established modality for the treatment of distal ureteral stones due to its clearance rate, pain control, and patient satisfaction while having minimal morbidity in comparison to other urologic interventions. In some studies, a combination of medications has been used, which we assessed in this network meta-analysis (NMA). METHODS: We conducted systematic searches in PubMed, Scopus, and Web of Science to identify relevant trials published between 2001 and 2024. We excluded articles that looked at MET for upper ureteral stone passage or after shock wave lithotripsy (SWL). NMA was performed to compare the effect of combination MET on stone expulsion rate (SER), stone expulsion time (SET), and need for analgesia. RESULTS: We included 19 studies with 2414 participants. NMA results revealed that the combination MET of α-blockers with PDE-5 inhibitors (OR = 2.7, CI = 1.80,4.05), corticosteroids (OR = 2.7, CI = 1.81,4.13), and phytotherapy (OR: 3.10, CI = 1.62,5.92) were more effective than α-blockers alone in SER. The combination MET of α-blockers with PDE-5 inhibitors (MD: -3.8, CI=-7.0, -0.5) showed significantly lower SET compared to α-blockers alone. Finally, combination MET of α-blockers with PDE-5 inhibitors (MD:1.0, CI = 0.4,1.7) and nifedipine with corticosteroids (MD:1.2, CI = 0.4,1.9) showed a significant decrease in analgesia use. CONCLUSIONS: The combination MET of α-blockers with PDE-5 inhibitors, corticosteroids, and phytotherapy increases the rate of stone clearance 2.7 to 3.1 times more than α-blockers alone. The other benefits of combination MET were lower expulsion time and less analgesia use that needs further studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".