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- Framing Bias in a Large Language Model: A Diagnostic Accuracy Study of Prompt Effects on ChatGPT’s Melanoma Classification- Mendeley Supplemental Figure 1. Representative examples of the test. Each image was presented six times under different instructions: a neutral baseline prompt, and five framed prompts. - Mendeley Supplemental File 1. Detailed Materials and methods (Study design, Model Access and Interaction, Dataset Details, Prompting Procedure, Outcome Measures and Statistical Analysis) of the study
- Gene expression data from Orthotopic human immune system reconstituted (HIR) patient-derived xenograft (PDX) PDAC mouse models treated with Ladarixin, Nivolumab, and the combination of the two drugsRNA-seq raw count matrix from Orthotopic human immune system reconstituted (HIR) patient-derived xenograft (PDX) PDAC mouse models. metadata file match sample IDs and treatment conditions
- DATASET_Psychopathological burden and coping strategies among Italian healthcare workers facing the COVID-19 emergency: data from the COMET collaborative network DATA from "Psychopathological burden and coping strategies among Italian healthcare workers facing the COVID-19 emergency: data from the COMET collaborative network "
- Lung ultrasound to detect pneumothorax in children evaluated for acute chest pain in the emergency department: a prospective study We addressed the accuracy of Lung Ultrasound (LUS) to detect pneumothorax in children with acute chest pain evaluated in the pediatric Emergency Department (pED). Methods We prospectively analyzed patients from 5 to 17 years of age with acute chest pain and clinical suspicion of pneumothorax (PNX) evaluated at a tertiary level pediatric hospital. After clinical examination and before Chest X-Ray (CXR), children underwent LUS to evaluate the presence of PNX. Results We enrolled 77 children, 44 (57,1 %) male, with median age of 10 years and 3 months (IQR 6 years and 9 months - 14 years and 2 months). Thirty (39%) children had interstitial lung disease; 20/77 (26%) had pneumonia with or without pleural effusions; 7/77 (9,1%) had thoracic trauma; 7/77 (9,1%) had a final diagnosis of myo/pericarditis and 13 (16,9%) received a final diagnosis of PNX. In all 13 patients LUS showed the “bar-code sign” while in 12 (92,3%) there was the lung point, giving a diagnosis of PNX. All cases were confirmed by CXR. The lung point had a sensitivity of 92,3% and a specificity of 100%, a positive predictive value of 100% and a negative predictive value of 98,4 % for the detection of PNX. The “bar-code sign” had a sensitivity of 100% and a specificity of 100%, a positive predictive value of 100% and a negative predictive value of 100% for the detection of PNX. Conclusions LUS is highly accurate in detecting or excluding pneumothorax in children with acute chest pain evaluated in the pediatric emergency department. Importantly, both lung-point and M-mode need to be performed when PNX is suspected.