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AI Predictive Model of Mortality and Intensive Care Unit Admission in the COVID-19 Pandemic: Retrospective Population Cohort Study of 12,000 Patients

AI Predictive Model of Mortality and Intensive Care Unit Admission in the COVID-19 Pandemic: Retrospective Population Cohort Study of 12,000 Patients

(A) Mortality predictor model and (B) ICU admission predictor metrics. AUC: area under the receiver operating characteristic curve; TNR: true negative rate; TPR: true positive rate. Global predictors ROCs for mortality and ICU admissions. Model validation using new records confirms the performance of the global models. With validation hospitalized dataset, the exitus global model has a sensitivity of 0.86, specificity of 0.74, PPV of 0.24 (18/79), NPV of 0.98 (160/163), and AUC of 0.85.

Jose Manuel Ruiz Giardin, Óscar Garnica, Nieves Mesa Plaza, Juan Víctor SanMartín López, Ana Farfán Sedano, Elena Madroñal Cerezo, Miguel Ángel Duarte Millán, Aida Izquierdo Martínez, Luis Rivas, Marta Rivilla, Alejandro Morales Ortega, Begoña Frutos Pérez, Cristina De Ancos Aracil, Ruth Calderón, Guillermo Soria Fernandez, Jorge Marrero Francés, David Bernal Bello, Jose Ángel Satué Bartolomé, María Toledano Macías, Sara Piedrabuena García, Marta Guerrero Santillán, Rafael Cristóbal, Belen Mora, Laura Velázquez Ríos, Vanesa García de Viedma, Paula Cuenca Ruiz, Ibone Ayala Larrañaga, Lorena Carpintero, Celia Lara, Alvaro Ricardo Llerena, Virginia García Bermúdez, Gema Delgado Cárdenas, Paloma Pardo Rovira, Elena Tejero Sánchez, Maria Jesús Domínguez García, Carolina Mariño, Cristina Bravo, Ana Ontañon, Mario García, Jose Ignacio Hidalgo Pérez, Santiago Prieto Menchero, Natalia González Pereira, Sonia Gonzalo Pascua, Jorge Tarancón Rey, Luis Antonio Lechuga Suárez, FUENCOVID

J Med Internet Res 2025;27:e70674

Usefulness of Mixed Reality in Surgical Treatment: Delphi Study

Usefulness of Mixed Reality in Surgical Treatment: Delphi Study

To facilitate the interpretation of the results, the Kendall tau-b correlation was chosen for this study due to its effectiveness in analyzing associations between ordinal variables. This test is particularly advantageous for evaluating the concordance between rankings because it accurately accounts for tied ranks. Kendall tau-b provides a precise measure of the association between variables, making it an ideal tool for ordinal data analysis.

Renato Magalhães, Ana Carolina Lima, António Marques, Javier Pereira, Lúcio Lara Santos

J Med Internet Res 2025;27:e69964

ChatGPT-4–Driven Liver Ultrasound Radiomics Analysis: Diagnostic Value and Drawbacks in a Comparative Study

ChatGPT-4–Driven Liver Ultrasound Radiomics Analysis: Diagnostic Value and Drawbacks in a Comparative Study

Seventy B-mode grayscale ultrasound images acquired from validated rat liver disease models [30-32] were used for analysis. The images were distributed across 3 categories of liver health: fibrosis (n=31), steatosis (fatty liver) (n=18), and normal (n=21). To maintain consistency and reliability in the analysis, the imaging parameters were standardized, including transducer frequency, gain settings, imaging depth, focus, and dynamic range.

Laith R Sultan, Shyam Sunder B Venkatakrishna, Sudha A Anupindi, Savvas Andronikou, Michael R Acord, Hansel J Otero, Kassa Darge, Chandra M Sehgal, John H Holmes

JMIR AI 2025;4:e68144

Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data

Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data

County B, on the east side of the state, is an urban county, with over 370,000 people. The jail had 404 beds and 8300 bookings in 2019. These jails vary in screening methods for suicide risk at jail booking, which reflects variability nationally. County A uses several scripted questions about current and past suicide ideation or attempts, and County B uses several scripted questions and a truncated version of the Columbia Suicide Severity Rating Scale [25]. The 2 jail populations vary demographically.

Erin B Comartin, Grant Victor, Athena Kheibari, Brian K Ahmedani, Bethany Hedden-Clayton, Richard N Jones, Ted R Miller, Jennifer E Johnson, Lauren M Weinstock, Sheryl Kubiak

JMIR Res Protoc 2025;14:e68517