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PD04-08 ASSESSING THE THRESHOLD FOR INJURY WHEN PASSING AN URETERAL ACCESS SHEATH: HOW MUCH FORCE IS TOO MUCH?

2019· article· en· W2940905262 on OpenAlexaboutno aff
Shlomi Tapiero, Kamaljot S. Kaler, Linda My Huynh, Mitchell O’Leary, Vinay Cooper, Zachary A. Valley, Renai Yoon, Roshan M. Patel, Zhamshid Okhunov, Michael Klopfer, Jaime Landman, Ralph V. Clayman

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSoftware deploymentUreteroscopyPower (physics)Operations researchSurgeryComputer scienceUreterPhysics

Abstract

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You have accessJournal of UrologySurgical Technology & Simulation: Instrumentation & Technology I (PD04)1 Apr 2019PD04-08 ASSESSING THE THRESHOLD FOR INJURY WHEN PASSING AN URETERAL ACCESS SHEATH: HOW MUCH FORCE IS TOO MUCH? Shlomi Tapiero*, Kamaljot S. Kaler, Linda M. Huynh, Mitchell L. O'Leary, Vinay G. Cooper, Zachary A. Valley, Renai H. Yoon, Roshan M. Patel, Zhamshid Okhunov, Michael Klopfer, Jaime Landman, and Ralph V. Clayman Shlomi Tapiero*Shlomi Tapiero* , Kamaljot S. KalerKamaljot S. Kaler , Linda M. HuynhLinda M. Huynh , Mitchell L. O'LearyMitchell L. O'Leary , Vinay G. CooperVinay G. Cooper , Zachary A. ValleyZachary A. Valley , Renai H. YoonRenai H. Yoon , Roshan M. PatelRoshan M. Patel , Zhamshid OkhunovZhamshid Okhunov , Michael KlopferMichael Klopfer , Jaime LandmanJaime Landman , and Ralph V. ClaymanRalph V. Clayman View All Author Informationhttps://doi.org/10.1097/01.JU.0000555056.73845.53AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Ureteral injury is a potential complication of ureteral access sheath (UAS) deployment. The amount of force that results in clinical ureteral injury has yet to be defined. Herein, we present our clinical experience using a novel UAS Force Sensor (UAS-FS) during routine ureteroscopy. METHODS: UAS deployment force was measured with a UAS-FS, developed in collaboration with engineers at our institution (Figure 1). Continuous UAS-FS measurements were recorded from the urethral meatus until either the UAS reached the desired ureteral deployment site or 10 Newtons (N) was recorded. A 16 Fr UAS was used in each initial attempt. Fluoroscopic images were obtained at 2 N, 4 N, 6 N, 8 N, and 10 N. In general, if 8 N was reached, the 16 Fr UAS was exchanged for a 14 Fr or 11 Fr UAS. Ureteroscopic assessment of the ureter at the end of each case was quantitated using the post-ureteroscopic lesion scale (PULS). RESULTS: UAS deployment force was measured in 72 ureters among 65 patients. The 16 Fr UAS was deployed in 51 ureters (71%) at a maximal force of 5.4 N (1.9-11.6). In the remaining cases, a 14 Fr or 11 Fr UAS was passed in 19% and 10% with a maximal force of 4.6 N (1.4-9.1) and 3.1 N (1.2-5.0), respectively (p=0.08). Maximum peak force was most commonly recorded (31%) at the distal ureter. PULS 0, 1, and 2 were recorded in 31%, 36%, and 15% of cases, respectively (not recorded in 17%) (Table 1). A single PULS 3 was noted following deployment of a 14 Fr UAS at a peak pressure of 8.9 N. Of note, when the force applied was <5 N, the PULS score was consistently ≤1. On multivariate analysis, successful deployment of a 16 Fr UAS was associated with pre-stented ureters (p=0.04). Preoperative treatment with tamsulosin was not associated with lower PULS (p=0.32), lower peak force applied (p=0.39), or with an increase in deployment of the 16 Fr UAS (p=0.45). CONCLUSIONS: Using an UAS-FS, ureteral access deployment force could be continuously measured in the clinical setting. Limiting the deployment force to <5 N consistently resulted in a PULS score of ≤1. Source of Funding: none Orange, CA; Calgary, Canada; Orange, CA; Irvine, CA; Orange, CA© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e76-e77 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Shlomi Tapiero* More articles by this author Kamaljot S. Kaler More articles by this author Linda M. Huynh More articles by this author Mitchell L. O'Leary More articles by this author Vinay G. Cooper More articles by this author Zachary A. Valley More articles by this author Renai H. Yoon More articles by this author Roshan M. Patel More articles by this author Zhamshid Okhunov More articles by this author Michael Klopfer More articles by this author Jaime Landman More articles by this author Ralph V. Clayman More articles by this author Expand All Advertisement PDF downloadLoading ...

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.113
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.1130.062

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 head0.054
GPT teacher head0.359
Teacher spread0.305 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2019
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