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European surgical institutions have been recently criticized for the insufficient endorsement of standardized robotic training curricula. In the present study, we report the results of a nationwide survey on the status of robotic surgery training and satisfaction across trainees from multiple surgical specialties in Italy. A modified-Delphi consensus was implemented with n = 41 multiple-choice or open-ended question surveys later distributed to Residents, Fellows, PhD students, Young Attendings or Assistant Professors, in n = 10 different surgical specialties. Demographics, program characteristics, exposure to robotic surgery, and satisfaction with robotic training were explored. Composite variables were created, and multivariable regression modeling adopted to explore variables associated with higher surgical training satisfaction. Similarly, predictors of minimum requirements necessary for designation as a ‘reference center’ for robotic surgical training institutions were further explored. A total of n = 735 trainees participated and were included for final analysis. The vast majority were male (63.5%), residents (52.1%), from University Hospitals (53.7%), and from the Central Regions of Italy (47.9%). General Surgery was the most represented specialty (44.4%) followed by Urology (26%). Availability of a dual robotic console, simulation software, and scheduled lectures were reported in 45.6%, 58.2%, and 23.4% respectively. Most respondents (61.6%) reported that the robotic console was exclusively operated by senior/experienced attendings, and the majority (88.3%) believed that the lack of standardized robotic exposure negatively impacted their training. On univariable analysis, Urology achieved the highest satisfaction with robotic training (median score 6 [IQR: 5–8], p < 0.001). However, on multivariable modeling, the surgical specialty did not impact overall satisfaction, which was instead influenced by simulation software availability (adjusted Odds Ratio [aOR]: 1.72, 95% CI 1.22–2.44), dedicated lectures (aOR: 1.72, 95% CI 1.16–2.56), higher volume of robotic-assisted procedures per center (aOR: 9.69, 95% CI 5.62–16.70), and trainee with more active role during robotic cases (aOR: 2.43, 95% CI 1.29–4.57). Finally, centers with urology specialty (p < 0.001), > 400 procedures/year (aOR: 1.92, 95% CI 1.01–4.51), > 1 robotic platform (aOR: 1.29, 95% CI 1.07–1.55), and providing robotics fellowship/masters programs (aOR: 1.43, 95% CI 0.99–2.36) were more likely to reach the minimum requirements as per robotic training ‘reference center’ definition. We highlighted a nationwide lack of standardized training pathways, limited mentoring opportunities, and widespread dissatisfaction among robotic surgery trainees. These findings may guide robotic programs in recognizing unmet educational needs and implementing strategies to develop equitable, structured, and standardized curricula for robotic surgical training across the multiple robotic-assisted surgical specialties.
Robotic surgery across surgical specialties: an Italian survey on implementation, training, and satisfaction levels among trainees and young attendings: on behalf of the Italian Multispecialty Society of Young Surgeons (SPIGC) / Del Giudice, F., Santarelli, V., Fiori, G., Mastrovito, S., Marinucci, B.T., Mantovani, S., Bolletta, A., Rovere, G., Di Giorgio, D., Oddi, F.M., Carino, D., Di Bartolomeo, M., Chung, B.I., Crocetto, F., Maruccia, M., Meniconi, R., Pasculli, A., Angrisani, M., Null, N., Abate, M., et al.. - In: UPDATES IN SURGERY. - ISSN 2038-131X. - (2026). [10.1007/s13304-026-02747-y]
Robotic surgery across surgical specialties: an Italian survey on implementation, training, and satisfaction levels among trainees and young attendings: on behalf of the Italian Multispecialty Society of Young Surgeons (SPIGC)
European surgical institutions have been recently criticized for the insufficient endorsement of standardized robotic training curricula. In the present study, we report the results of a nationwide survey on the status of robotic surgery training and satisfaction across trainees from multiple surgical specialties in Italy. A modified-Delphi consensus was implemented with n = 41 multiple-choice or open-ended question surveys later distributed to Residents, Fellows, PhD students, Young Attendings or Assistant Professors, in n = 10 different surgical specialties. Demographics, program characteristics, exposure to robotic surgery, and satisfaction with robotic training were explored. Composite variables were created, and multivariable regression modeling adopted to explore variables associated with higher surgical training satisfaction. Similarly, predictors of minimum requirements necessary for designation as a ‘reference center’ for robotic surgical training institutions were further explored. A total of n = 735 trainees participated and were included for final analysis. The vast majority were male (63.5%), residents (52.1%), from University Hospitals (53.7%), and from the Central Regions of Italy (47.9%). General Surgery was the most represented specialty (44.4%) followed by Urology (26%). Availability of a dual robotic console, simulation software, and scheduled lectures were reported in 45.6%, 58.2%, and 23.4% respectively. Most respondents (61.6%) reported that the robotic console was exclusively operated by senior/experienced attendings, and the majority (88.3%) believed that the lack of standardized robotic exposure negatively impacted their training. On univariable analysis, Urology achieved the highest satisfaction with robotic training (median score 6 [IQR: 5–8], p < 0.001). However, on multivariable modeling, the surgical specialty did not impact overall satisfaction, which was instead influenced by simulation software availability (adjusted Odds Ratio [aOR]: 1.72, 95% CI 1.22–2.44), dedicated lectures (aOR: 1.72, 95% CI 1.16–2.56), higher volume of robotic-assisted procedures per center (aOR: 9.69, 95% CI 5.62–16.70), and trainee with more active role during robotic cases (aOR: 2.43, 95% CI 1.29–4.57). Finally, centers with urology specialty (p < 0.001), > 400 procedures/year (aOR: 1.92, 95% CI 1.01–4.51), > 1 robotic platform (aOR: 1.29, 95% CI 1.07–1.55), and providing robotics fellowship/masters programs (aOR: 1.43, 95% CI 0.99–2.36) were more likely to reach the minimum requirements as per robotic training ‘reference center’ definition. We highlighted a nationwide lack of standardized training pathways, limited mentoring opportunities, and widespread dissatisfaction among robotic surgery trainees. These findings may guide robotic programs in recognizing unmet educational needs and implementing strategies to develop equitable, structured, and standardized curricula for robotic surgical training across the multiple robotic-assisted surgical specialties.
Robotic surgery across surgical specialties: an Italian survey on implementation, training, and satisfaction levels among trainees and young attendings: on behalf of the Italian Multispecialty Society of Young Surgeons (SPIGC) / Del Giudice, F., Santarelli, V., Fiori, G., Mastrovito, S., Marinucci, B.T., Mantovani, S., Bolletta, A., Rovere, G., Di Giorgio, D., Oddi, F.M., Carino, D., Di Bartolomeo, M., Chung, B.I., Crocetto, F., Maruccia, M., Meniconi, R., Pasculli, A., Angrisani, M., Null, N., Abate, M., et al.. - In: UPDATES IN SURGERY. - ISSN 2038-131X. - (2026). [10.1007/s13304-026-02747-y]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/3076557
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.