1503 VALIDATION OF A PARTIAL NEPHRECTOMY BENCH MODEL DEVELOPED VIA A NOVEL MATERIAL ENGINEERING PROCESS
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Résumé
You have accessJournal of UrologyTechnology & Instruments: Surgical Education & Skills Assessment/Ureteroscopy II1 Apr 20121503 VALIDATION OF A PARTIAL NEPHRECTOMY BENCH MODEL DEVELOPED VIA A NOVEL MATERIAL ENGINEERING PROCESS Abdulaziz Alamri, Alym Abdulla, John Madjeruh, and Edward D. Matsumoto Abdulaziz AlamriAbdulaziz Alamri Hamilton, Canada More articles by this author , Alym AbdullaAlym Abdulla Hamilton, Canada More articles by this author , John MadjeruhJohn Madjeruh Hamilton, Canada More articles by this author , and Edward D. MatsumotoEdward D. Matsumoto Hamilton, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.1270AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES We previously determined the mean tear strength and resistance of human kidneys and used this data to develop a high-fidelity partial nephrectomy model with similar tissue characteristics to that of a human. Our objective in this next phase, was to test the validity of this new partial nephrectomy bench model. METHODS A questionnaire evaluating face and content validity was distributed to urology staff, fellows and residents at a single institute. The questionnaire assessed the utility of the model as a surgical education tool using a 5-point scale. It asked participants to score the anatomical representation of the kidney model and the cutting, suturing, knot-tying and tissue tearing characteristics compared to a human kidney. Participants' opinion of the model's value as a training tool was also assessed. Participant level of training and clinical experience in performing laparoscopic and open surgery were collected. RESULTS Twenty participants assessed the model and completed the questionnaire (8 staff, 4 fellows, 5 senior residents and 3 junior residents). Eighteen participants (90%) agreed or strongly agreed that the model was a good representation of a human kidney and tumor and two (10%) participants were neutral in opinion. Sixteen participants (80%) agreed or strongly agreed that cutting through the model was similar to that of human kidney tissue, two (10%) were neutral and two (10%) disagreed in opinion. The median suturing score (out of 5) on the model were as follows: needle insertion=4, needle driving=3.5, knot/tying=4 and tissue tear strength=4. Overall, 19 participants (95%) agreed or strongly agreed that the model would help in laparoscopic training and 15 (75%) agreed or strongly agreed it would help in open surgical training. All participants would recommend use this model for resident training. We compared the responses of residents versus staff for all the above parameters and found no statistically significant difference (p>0.05). CONCLUSIONS Our partial nephrectomy model engineered using actual measures of tear strength and resistance of a real kidney demonstrates good face and content validity. Both experts and novice felt that this model was realistic and had potential educational utility. Validation of training utilizing this model will be the next step in our research. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e608 Peer Review Report Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Abdulaziz Alamri Hamilton, Canada More articles by this author Alym Abdulla Hamilton, Canada More articles by this author John Madjeruh Hamilton, Canada More articles by this author Edward D. Matsumoto Hamilton, Canada More articles by this author Expand All Advertisement 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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».