Medical Expulsive Therapy as an Adjunct to Improve Shockwave Lithotripsy Outcomes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Modern shockwave lithotripsy (SWL) is associated with inferior results compared with the original Dornier HM3. To enhance SWL outcomes, improved patient selection based on radiographic features and modulation of shockwave delivery rate have been used. A growing body of evidence demonstrates the positive effect of medical expulsive therapy (MET) to improve spontaneous passage of urinary calculi. The purpose of this review is to tabulate the current available data that examine the addition of MET to SWL to enhance outcomes. MATERIALS AND METHODS: MEDLINE was searched with a strategy developed in conjunction with a medical librarian. Trials were included if patients were randomized to receive either a medical expulsive agent or placebo or standard therapy after SWL. Study quality was assessed according to the Cochrane Renal Group criteria. The data were analyzed using RevMan meta-analysis software. Subgroup analysis was performed with respect to MET agent used, stone size, and duration of follow-up. RESULTS: Four randomized trials were identified. MET agents varied, with two trials using tamsulosin, one using nifedipine, and a single trial using Phyllanthus niruri extract. Two trials included patients with renal calculi, one had patients with ureteral calculi, and the fourth included patients with both ureteral and renal calculi. The pool results of the four trials included 212 patients who received MET and 206 who received placebo. The absolute risk difference of a successful outcome after SWL with the addition of MET was significantly superior to control at 17% (95% confidence interval [CI] 9%-24%); means six patients need to be treated with MET to prevent a single unsuccessful SWL of six (95% CI 4-11). The effect of MET post-SWL was even more pronounced for stones larger than 10 mm with an absolute risk difference of 26% (95% CI, 9%-43%). CONCLUSIONS: MET post-SWL results in a significant increase in successful treatment outcomes. Further powered, randomized studies are encouraged.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".