Endoscopic Combined Intrarenal Surgery: best practices and future perspectives
Bibliographic record
Abstract
INTRODUCTION: Endoscopic Combined Intrarenal Surgery (ECIRS) has emerged as a promising technique for the management of large and complex kidney stones, potentially offering advantages over traditional Percutaneous Nephrolithotomy (PCNL). This study aims to evaluate best practices, outcomes, and future perspectives associated with ECIRS. MATERIALS AND METHODS: A comprehensive PubMed search was conducted from 2008 to 2024, using MESH terms and the following key words: "ECIRS" and "Endoscopic Combined Intrarenal Surgery" The search yielded 157 articles, including retrospective cohort studies, two randomized controlled trials (RCTs), and four meta-analyses comparing ECIRS with PCNL. Most important findings were summarized regarding indications, patient positioning, kidney access, tract size, surgical outcomes, and complications. RESULTS: ECIRS demonstrated higher stone-free rate, lower complication rate, and a reduced need for multiple procedures compared to traditional PCNL. Additionally, ECIRS has the potential to integrate new technologies to further enhance outcomes. CONCLUSION: ECIRS demonstrates significant advantages in the management of large kidney stones. Future research should focus on well-designed RCTs to provide robust evidence of its efficacy, safety, and cost-effectiveness, potentially establishing ECIRS as the first option treatment for complex kidney stones.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".