Worldwide Trends of Urinary Stone Disease Treatment Over the Last Two Decades: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Numerous studies have reported on regional or national trends of stone disease treatment. However, no article has yet examined the global trends of intervention for stone disease. METHODS AND MATERIALS: A systematic review of articles from 1996 to September 2016 for all English language articles reporting on trends of surgical treatment of stone disease was performed. Authors were contacted in the case of data not being clear. If the authors did not reply, data were estimated from graphs or tables. Results were analyzed using SPSS version 21, and trends were analyzed using linear regression. RESULTS: Our systematic review yielded 120 articles, of which 8 were included in the initial review. This reflected outcomes from six countries with available data: United Kingdom, United States, New Zealand, Australia, Canada, and Brazil. Overall ureteroscopy (URS) had a 251.8% increase in total number of treatments performed with the share of total treatments increasing by 17%. While the share of total treatments for percutaneous nephrolithotomy (PCNL) remained static, the share for extracorporeal shockwave lithotripsy and open surgery fell by 14.5% and 12%, respectively. There was significant linear regression between rising trends of total treatments year on year for URS (p < 0.001). CONCLUSION: In the last two decades, the share of total treatment for urolithiasis across the published literature has increased for URS, stable for PCNL, and decreased for lithotripsy and open surgery.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".