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Record W4413107689 · doi:10.1177/08927790251368775

Robotic-Assisted Electromagnetic Guidance Improves Success of Percutaneous Access for Nephrolithotomy: A Study of Novices and Experts

2025· article· en· W4413107689 on OpenAlexaff
Christopher Ballantyne, Kevin Wymer, Nancy L. Sehgel, Ben H. Chew, Fuad Elkhoury, Sri Sivalingam, Matthew D. Dunn, Michael S. Borofsky, Mitchell R. Humphreys

Bibliographic record

VenueJournal of Endourology · 2025
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePercutaneous nephrolithotomyPercutaneousSurgeryMedical physicsGeneral surgeryUrology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.343
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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