Robotic-Assisted Electromagnetic Guidance Improves Success of Percutaneous Access for Nephrolithotomy: A Study of Novices and Experts
Bibliographic record
Abstract
Introduction: Percutaneous nephrolithotomy (PCNL) has been a mainstay treatment for large stone burdens since the 1980s, historically offering improved stone-free rates over retrograde intrarenal surgery (RIRS). Gaining optimal access into the renal collecting system can be challenging, requiring advanced skills or interventional radiology assistance. The learning curve for fluoroscopic and ultrasonography access can be steep, with only a minority of PCNL access performed by urologists in the United States. This study compares robotic-assisted electromagnetic (EM) guidance to traditional fluoroscopy for obtaining percutaneous renal access between cohorts of novice and expert urologists. Methods: Ten novices and five expert urologists used robotic-assisted EM guidance to obtain access in a modified supine position compared with using fluoroscopy in a traditional prone position in human cadavers. Primary success was defined as papillary access. Performance metrics, including number of puncture attempts, time to access, radiation exposure, and participant confidence, were compared between novices and experts. Results: Robotic-assisted EM guidance improved success rates for both novices (100% vs 70%) and experts (93% vs 87%) compared with fluoroscopy. Novices showed greater accuracy using robotic assistance (97% vs 37%). The number of insertion attempts decreased with robotic guidance for both groups (novices: 3.42 ± 0.44 vs 1.47 ± 0.19; experts: 2.13 ± 0.36 vs 1.40 ± 0.24; p < 0.002). EM guidance ( p < 0.05) and experience ( p < 0.05) significantly reduced the time from needle insertion to access (novices: 12.86 ± 2.41 minutes vs 4.49 ± 0.96 minutes; experts: 4.90 ± 1.40 minutes vs 4.09 ± 1.12 minutes). Radiation exposure was notably lower with EM guidance (novices, 1.12 ± 0.17 mGy vs 4.86 ± 0.70 mGy; experts, 0.69 ± 0.12 mGy vs 4.11 ± 1.21 mGy; p < 0.001). Novices felt more confident (5[3–5] vs 2[1–4], p < 0.001) and at ease (5[3–5] vs 2.75[1–3], p < 0.001) with EM guidance. Conclusion: Robotic-assisted EM guidance improves percutaneous access success, reduces attempts and radiation exposure, and enhances novice confidence and accuracy. This technology could enable urologists to more effectively and safely perform PCNL, especially for less experienced practitioners.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".