PD27-12 TOWARDS OPTIMIZING SIMULATION-BASED TRAINING FOR PERCUTANEOUS NEPHROLITHOTOMY: A PROSPECTIVE COMPARATIVE STUDY
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Résumé
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 ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».