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PD27-12 TOWARDS OPTIMIZING SIMULATION-BASED TRAINING FOR PERCUTANEOUS NEPHROLITHOTOMY: A PROSPECTIVE COMPARATIVE STUDY

2019· article· en· W2942360226 on OpenAlexaboutno aff
Ahmed Ibrahim, Yasser A. Noureldin, Sero Andonian

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePercutaneous nephrolithotomyTraining (meteorology)Simulation trainingPercutaneousProspective cohort studyUrologySurgerySimulation

Abstract

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You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment I (PD27)1 Apr 2019PD27-12 TOWARDS OPTIMIZING SIMULATION-BASED TRAINING FOR PERCUTANEOUS NEPHROLITHOTOMY: A PROSPECTIVE COMPARATIVE STUDY Ahmed Ibrahim*, Yasser Noureldin, and Sero Andonian Ahmed Ibrahim*Ahmed Ibrahim* More articles by this author , Yasser NoureldinYasser Noureldin More articles by this author , and Sero AndonianSero Andonian More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555975.72575.3bAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Obtaining the percutaneous renal access is considered the critical step in performing percutaneous nephrolithotomy (PCNL). The aim was to assess the transfer of percutaneous access skills gained from training on the PERC MentorTM simulator to the operating room. METHODS: After obtaining ethics approval, urology Post-Graduate Trainees (PGTs) from Post-Graduate Years (PGY) 4 and 5 were recruited. Participants received educational demonstration on how to perform the PCA using bull's eye technique prior to being asked to perform task 5 on the PERC Mentor simulator (Simbionix, Cleveland, Ohio, USA), where they had to correctly puncture the middle calyx over a stone in a left kidney model. All participants were assessed objectively by the PERC Mentor simulator and subjectively by the validated Percutaneous Nephrolithotomy-Global Rating Scale (PCNL-GRS) tool. The learning curve was assessed in terms of reaching competency in performing the PCA with plateauing in PCNL-GRS score, operative and fluoroscopy times, and absence of complications. To assess the transfer of PCA skills from the PERC Mentor simulator to the operating room (OR), all participants were asked to perform PCA inside the OR and were assessed using the same PCNL-GRS score. The relationship between the PCNL-GRS score, operative time, fluoroscopy time, and complications on the simulator and inside the OR was addressed. RESULTS: Eight urology PGTs (5 PGY-4 and 3 PGY-5), with median age of 30 (27.8-32.3) years and without prior PCNL experience, participated in this study. Participants performed a total of 72 PCA procedures, with mean operative time of 155.8±14 seconds, and mean fluoroscopy time of 89.9±9 seconds, mean number of attempts to puncture the PCS of 1.8±0.3, mean pelvi-calyceal system (PCS) perforation of 0.88±0.2, mean vascular injury of 0.5±0.08, and PCNL-GRS score of 21.8±0.6. Competency in task 5 on the PERC Mentor simulator was achieved after five trials in terms of the PCNL-GRS score, 14 trials in terms of the operative and fluoroscopy times and PCS perforations, and 9 trials in terms of the vascular injury. Furthermore, participants performed 20 PCA procedures with mean time to achieve successful puncture of 120±15 seconds, mean fluoroscopy time of 27.2±4.7 seconds, mean attempts to puncture the PCS of 1.5±0.2, mean PCNL-GRS score of 20.3±0.9. However, one case (5%) of colon perforation was encountered and 2 cases (10%) was associated with failed puncture. CONCLUSIONS: While training on PERC MentorTM simulator is associated with improving the learning curve of the trainees in terms of operative and fluoroscopy times, the optimal PCNL simulator is still required in order to overcome certain PCNL challenges and reduce intraoperative complications. Source of Funding: None Montreal, Canada; Benha, Egypt; Montreal, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e485-e486 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ahmed Ibrahim* More articles by this author Yasser Noureldin More articles by this author Sero Andonian More articles by this author Expand All Advertisement PDF downloadLoading ...

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 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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.075
GPT teacher head0.341
Teacher spread0.266 · 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 designNon-randomized trial
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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Published2019
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