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40 results on '"Signorelli C."'

1. COVID-19 pandemic impact on people with diabetes: results from a large representative sample of Italian older adults

Author

Amerio, A., Amore, M., Bertuccio, P., Bonaccio, M., Bosetti, C., Cavalieri d’Oro, L., Ciampichini, R., De Sena, R., Gallus, S., Gianfagna, F., Ghislandi, S., Ghulam, A., Iacoviello, L., Jarach, CM., Lugo, A., Mosconi, G., Odone, A., Rognoni, M., Serafini, G., Scala, M., Signorelli, C., Stival, C., Stuckler, D., Vigezzi, GP., Wang, Y., Zucchi, A., Vigezzi, Giacomo Pietro, Bertuccio, Paola, Bossi, Camilla Bonfadini, Amerio, Andrea, d’Oro, Luca Cavalieri, Derosa, Giuseppe, Iacoviello, Licia, Stuckler, David, Zucchi, Alberto, Lugo, Alessandra, Gallus, Silvano, and Odone, Anna

Published
2022
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7. Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature

Author

Scardoni, A, Balzarini, F, Signorelli, C, Cabitza, F, Odone, A, Scardoni A., Balzarini F., Signorelli C., Cabitza F., Odone A., Scardoni, A, Balzarini, F, Signorelli, C, Cabitza, F, Odone, A, Scardoni A., Balzarini F., Signorelli C., Cabitza F., and Odone A.

Abstract

Background: Healthcare-associated infections (HAIs) are the most frequent adverse events in healthcare and a global public health concern. Surveillance is the foundation for effective HAIs prevention and control. Manual surveillance is labor intensive, costly and lacks standardization. Artificial Intelligence (AI) and machine learning (ML) might support the development of HAI surveillance algorithms aimed at understanding HAIs risk factors, improve patient risk stratification, identification of transmission pathways, timely or real-time detection. Scant evidence is available on AI and ML implementation in the field of HAIs and no clear patterns emerges on its impact. Methods: We conducted a systematic review following the PRISMA guidelines to systematically retrieve, quantitatively pool and critically appraise the available evidence on the development, implementation, performance and impact of ML-based HAIs detection models. Results: Of 3445 identified citations, 27 studies were included in the review, the majority published in the US (n = 15, 55.6%) and on surgical site infections (SSI, n = 8, 29.6%). Only 1 randomized controlled trial was included. Within included studies, 17 (63%) ML approaches were classified as predictive and 10 (37%) as retrospective. Most of the studies compared ML algorithms’ performance with non-ML logistic regression statistical algorithms, 18.5% compared different ML models’ performance, 11.1% assessed ML algorithms’ performance in comparison with clinical diagnosis scores, 11.1% with standard or automated surveillance models. Overall, there is moderate evidence that ML-based models perform equal or better as compared to non-ML approaches and that they reach relatively high-performance standards. However, heterogeneity amongst the studies is very high and did not dissipate significantly in subgroup analyses, by type of infection or type of outcome. Discussion: Available evidence mainly focuses on the development and testing of HAIs detect

Published
2020

8. 529P Evaluating metastatic disease sites as a prognostic marker in patients receiving sequential treatment with regorafenib and trifluridine/tipiracil for refractory colorectal cancer: Survival outcomes from the multicenter retrospective “ReTrITA” study

Author

Signorelli, C., Calegari, M.A., Anghelone, A., Basso, M., Passardi, A., Frassineti, G.L., Zurlo, I.V., Angotti, L., Chilelli, M.G., Morelli, C., Dell'Aquila, E., Gemma, D., Corsi, D.C., Mazzuca, F., Zoratto, F., Morandi, M.G., Santamaria, F., Saltarelli, R., Dettori, M., and Ruggeri, E.M.

Published
2024
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9. Immunogenicity and early clinical outcome after two or three doses of SARS-CoV-2 mRNA-BNT162b2 vaccine in actively treated cancer patients: results from the prospective observational Vax-On-Third study

Author

Nelli, F., Giannarelli, D., Fabbri, A., Silvestri, M.A., Berrios, J. R. Giron, Virtuoso, A., Marrucci, E., Schirripa, M., Mazzotta, M., Onorato, A., Panichi, V., Topini, G., Pessina, G., Natoni, F., Signorelli, C., Chilelli, M.G., Primi, F., and Ruggeri, E.M.

Published
2022
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12. ItaLynch: an ongoing Italian study to evaluate the feasibility of mainstreaming the diagnosis of Lynch syndrome in colorectal cancer patients

Author

Puccini, A., Grillo, F., Fassan, M., Lonardi, S., Genuardi, M., Cannizzaro, R., Cavestro, G.M., Marmorino, F., Conca, V., Salvatore, L., Bergamo, F., Tosi, F., Morano, F., Daprà, V., Molica, C., Barana, D., Guglielmi, A., Signorelli, C., D’Amico, M., Zoratto, F., Iacono, D., Morabito, A., Martini, G., Fabbroncini, A., Duro, M., Bruera, G., Auriemma, A., Bonanni, B., Percesepe, A., Dono, M., Battistuzzi, L., Labianca, R., Boni, L., and Sciallero, S.

Published
2024
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13. 617P Predictive and prognostic impact of primary tumor location on sequential treatment with regorafenib and trifluridine/tipiracil at third line and beyond in metastatic colorectal cancer: A real-world multicenter retrospective analysis

Author

Signorelli, C., Calegari, M.A., Basso, M., Anghelone, A., Lucchetti, J., Minelli, A., Angotti, L., Zurlo, I.V., Schirripa, M., Chilelli, M.G., Morelli, C., Dell'Aquila, E., Gemma, D., Ribelli, M., Arrivi, G., Zoratto, F., Morandi, M.G., Santamaria, F., Saltarelli, R., and Ruggeri, E.M.

Published
2023
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17. P-236 Aflibercept-based and bevacizumab-based second-line regimens in patients with RAS mutant metastatic colorectal cancer: Propensity score weighted-analysis from a multicenter cohort

Author

Lucchetti, J., Angotti, L., Cortellini, A., Basso, M., Polito, M., Zoratto, F., Di Giacomo, E., Calegari, M., Lo Prinzi, F., Gemma, D., Signorelli, C., Veroli, M., Anghelone, A., Muscio, L. Galbato, Di Cocco, B., Trombetta, G., Guerriero, S., Schietroma, F., Vincenzi, B., and Tonini, G.

Published
2023
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18. P-249 Aflibercept-based and bevacizumab-based second-line regimens in patients with RAS/BRAF wild type metastatic colorectal cancer: Propensity score weighted-analysis from a multicenter cohort

Author

Angotti, L., Lucchetti, J., Cortellini, A., Basso, M., Polito, M., Zoratto, F., Di Giacomo, E., Calegari, M., Lo Prinzi, F., Gemma, D., Signorelli, C., Veroli, M., Trombetta, G., Guerriero, S., Muscio, L. Galbato, Di Cocco, B., Schietroma, F., Anghelone, A., Vincenzi, B., and Tonini, G.

Published
2023
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19. P-195 Influence of age on disease control rate of patients treated with regorafenib and trifluridine/tipiracil in refractory metastatic colorectal cancer: A multicenter retrospective real-world study

Author

Signorelli, C., Calegari, M., Basso, M., Anghelone, A., Lucchetti, J., Minelli, A., Angotti, L., Zurlo, I., Schirripa, M., Chilelli, M., Morelli, C., Dell’Aquila, E., Cosimati, A., Gemma, D., Ribelli, M., Corsi, D., Arrivi, G., Zoratto, F., Morandi, M., Santamaria, F., Saltarelli, R., and Ruggeri, E.

Published
2023
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21. 398P Effectiveness and safety of regorafenib and trifluridine/tipiracil in refractory metastatic colorectal cancer: A real-world multicenter retrospective study with focus on sequential treatment

Author

Signorelli, C., Schirripa, M., Chilelli, M.G., Calegari, M.A., Basso, M., Anghelone, A., Lucchetti, J., Minelli, A., Angotti, L., Morelli, C., Dell'Aquila, E., Cosimati, A., Gemma, D., Ribelli, M., Corsi, D.C., Arrivi, G., Zoratto, F., Morandi, M.G., Santamaria, F., Saltarelli, R., and Ruggeri, E.M.

Published
2022
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24. Corrigendum to “ACL reconstruction with lateral plasty reduces translational and rotatory laxity compared to anatomical single bundle and non-anatomical double bundle surgery: An in vivo kinematic evaluation with navigation system.” [Clin. Biomech. (Bristol, Avon) 2019 Oct; 69:1–8. doi:10.1016/j.clinbiomech.2019.06.012. Epub 2019 Jun 14]

Author

Grassi, A., Signorelli, C., Lucidi, G.A., Raggi, F., Macchiarola, L., Roberti Di Sarsina, T., Marcheggiani Muccioli, G.M., Filardo, G., and Zaffagnini, S.

Published
2020
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39. Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature

Author

Anna Odone, A Scardoni, Federico Cabitza, Carlo Signorelli, Federica Balzarini, Scardoni, A, Balzarini, F, Signorelli, C, Cabitza, F, Odone, A, Scardoni, A., Balzarini, F., Signorelli, C., Cabitza, F., and Odone, A.

Subjects
0301 basic medicine, medicine.medical_specialty, Artificial intelligence, Standardization, 030106 microbiology, Infection control, Logistic regression, lcsh:Infectious and parasitic diseases, law.invention, 03 medical and health sciences, 0302 clinical medicine, Randomized controlled trial, Risk Factors, law, Health care, Machine learning, medicine, Humans, Surgical Wound Infection, lcsh:RC109-216, Computer Simulation, 030212 general & internal medicine, Retrospective Studies, Cross Infection, business.industry, lcsh:Public aspects of medicine, Public health, Healthcare-associated infections (HAI), Public Health, Environmental and Occupational Health, lcsh:RA1-1270, General Medicine, Identification (information), Infectious Diseases, Systematic review, business, Predictive modelling
Abstract

Background Healthcare-associated infections (HAIs) are the most frequent adverse events in healthcare and a global public health concern. Surveillance is the foundation for effective HAIs prevention and control. Manual surveillance is labor intensive, costly and lacks standardization. Artificial Intelligence (AI) and machine learning (ML) might support the development of HAI surveillance algorithms aimed at understanding HAIs risk factors, improve patient risk stratification, identification of transmission pathways, timely or real-time detection. Scant evidence is available on AI and ML implementation in the field of HAIs and no clear patterns emerges on its impact. Methods We conducted a systematic review following the PRISMA guidelines to systematically retrieve, quantitatively pool and critically appraise the available evidence on the development, implementation, performance and impact of ML-based HAIs detection models. Results Of 3445 identified citations, 27 studies were included in the review, the majority published in the US (n = 15, 55.6%) and on surgical site infections (SSI, n = 8, 29.6%). Only 1 randomized controlled trial was included. Within included studies, 17 (63%) ML approaches were classified as predictive and 10 (37%) as retrospective. Most of the studies compared ML algorithms’ performance with non-ML logistic regression statistical algorithms, 18.5% compared different ML models’ performance, 11.1% assessed ML algorithms’ performance in comparison with clinical diagnosis scores, 11.1% with standard or automated surveillance models. Overall, there is moderate evidence that ML-based models perform equal or better as compared to non-ML approaches and that they reach relatively high-performance standards. However, heterogeneity amongst the studies is very high and did not dissipate significantly in subgroup analyses, by type of infection or type of outcome. Discussion Available evidence mainly focuses on the development and testing of HAIs detection and prediction models, while their adoption and impact for research, healthcare quality improvement, or national surveillance purposes is still far from being explored.

Published
2020

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