Pneumatic vs laser lithotripsy for mid-ureteric stones: Clinical and cost effectiveness results of a prospective trial in a developing country
Bibliographic record
Abstract
Objective To compare the management of large ureteric stones (>10 mm) with ureterorenoscopy (URS) and laser or pneumatic lithotripsy, and their associated costs.Patients and methods Our prospective study followed the tenets of the Declaration of Helsinki and included 101 patients with large mid-ureteric stones eligible for URS and lithotripsy, and was conducted between January 2018 and August 2019. Patients were randomly divided into two groups: Group 1 had laser lithotripsy, while the Group 2 had lithotripsy using a pneumatic energy source.Results Operative time was significantly longer in cases using pneumatic lithotripsy (P < 0.001). The stone-free rate (SFR) on the first postoperative day was 94% and 92.5% for laser and pneumatic lithotripsy respectively, and there were no statistically significant differences in terms of early (day 1) or late (day 30) SFRs between the groups. Complications were classified according to the Clavien–Dindo Grading System, all complications were Grade <III, with no statistically significant difference between the groups (P = 0.742). The use of pneumatic lithotripsy had lower treatment costs. The number of auxiliary procedures required to reach a stone-free status was statistically equivalent in both groups.Conclusion The type of lithotripsy did not affect the SFR or complications. However, laser lithotripsy was much more expensive than pneumatic lithotripsy.Abbreviations KUB: plain abdominal radiograph of the kidneys, ureters and bladder; SFR: stone-free rate; SWL: shockwave lithotripsy; URS: Ureterorenoscopy; US: ultrasonography
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".