PD27-12 TOWARDS OPTIMIZING SIMULATION-BASED TRAINING FOR PERCUTANEOUS NEPHROLITHOTOMY: A PROSPECTIVE COMPARATIVE STUDY
Bibliographic record
Abstract
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 ...
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".