Efficacy of Pregabalin, Solifenacin, or Combination Therapy for Ureteral Stent Related Symptoms: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: The Double-J (DJ) ureteral stent is essential in urology but can lead to Ureteral Stent-Related Symptoms (USRS), prompting research into various therapies to enhance patient comfort. The purpose of this study is to assess the efficacy of pregabalin, solifenacin, or combined therapy on ureteral stent-related symptoms. Materials and Methods: We conducted thorough searches in four databases, which included PubMed, Cochrane, EBSCO, and ProQuest. PRISMA Guideline 2020 was applied in this study. The risk of bias was assessed using Newcastle-Ottawa Scale and Cochrane Risk of Bias 2.0. Results: Ten studies consisting of 1477 participants were included in this study. Solifenacin monotherapy could significantly decrease total USSQ (mean difference (MD) -16.62; p=0.001), urinary symptoms (MD -9.16; p=0.002), and sexual matters (MD -0.81; p=0.002). Pregabalin monotherapy could significantly decrease pain (MD -7.29; p<0.00001). Compared to solifenacin monotherapy, combination therapy of pregabalin and solifenacin could significantly decrease total USSQ (MD -12.40; p <0.0001), urinary symptoms (MD -1.88; p=0.007), pain (MD -6.82; p<0.00001), sexual matters (MD -0.77; p <0.00001), and additional problems (MD -1.51; p=0.0007). Conclusion: Combination therapy of pregabalin and solifenacin had the best advantages in lowering USRS, especially urinary symptoms, pain, sexual matters, and some other additional problems.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".