Between human and AI: assessing the reliability of AI text detection tools

Large language models (LLMs) such as ChatGPT-4 have raised critical questions regarding their distinguishability from human-generated content. In this research, we evaluated the effectiveness of online detection tools in identifying ChatGPT-4 vs human-written text. A two texts produced by ChatGPT-4 using differing prompts and one text created by a human author were analytically assessed using the following online detection tools: GPTZero, ZeroGPT, Writer ACD, and Originality. The findings revealed a notable variance in the detection capabilities of the employed detection tools. GPTZero and ZeroGPT exhibited inconsistent assessments regarding the AI-origin of the texts. Writer ACD predominantly identified texts as human-written, whereas Originality consistently recognized the AI-generated content in both samples from ChatGPT-4. This highlights Originality’s enhanced sensitivity to patterns characteristic of AI-generated text. The study demonstrates that while automatic detection tools may discern texts generated by ChatGPT-4 significant variability exists in their accuracy. Undoubtedly, there is an urgent need for advanced detection tools to ensure the authenticity and integrity of content, especially in scientific and academic research. However, our findings underscore an urgent need for more refined detection methodologies to prevent the misdetection of human-written content as AI-generated and vice versa.</p

Image_1_Mid-Regional Proadrenomedullin (MR-proADM) and Microcirculation in Monitoring Organ Dysfunction of Critical Care Patients With Infection: A Prospective Observational Pilot Study.pdf

Introduction: Microvascular alterations are involved in the development of organ injury in critical care patients. Mid-regional proadrenomedullin (MR-proADM) may predict organ damage and its evolution. The main objective of this study was to assess the correlation between MR-proADM and microvascular flow index (MFI) in a small cohort of 20 adult critical care patients diagnosed with infection, sepsis, or septic shock. Further objectives were to evaluate the correlation between the clearance of MR-proADM and the variables of microcirculation and between MR-proADM and the Sequential Organ Failure Assessment (SOFA) score.Materials and Methods: This is a prospective observational pilot study. Inclusion criteria: consecutive adult patients admitted to intensive care unit (ICU) for or with infection-related illness. Daily measurement of MR-proADM and calculation of the SOFA score from admission in ICU to day 5. Repeated evaluations of sublingual microcirculation, collection of clinical data, and laboratory tests.Results: Primary outcome: MR-proADM was not significantly correlated to the MFI at admission in ICU. A clearance of MR-proADM of 20% or more in the first 24 h was related to the improvement of the MFIs and MFIt [percentual variation of the MFIs + 12.35 (6.01–14.59)% vs. +2.23 (−4.45–6.01)%, p = 0.005; MFIt +9.09 (4.53–16.26)% vs. −1.43 (−4.36–3.12)%, p = 0.002].Conclusion: This study did not support a direct correlation of MR-proADM with the MFI at admission in ICU; however, it showed a good correlation between the clearance of MR-proADM, MFI, and other microvascular variables. This study also supported the prognostic value of the marker. Adequately powered studies should be performed to confirm the findings.</p

Additional file 1 of Lung histopathologic clusters in severe COVID-19: a link between clinical picture and tissue damage

Additional file 1. List of clinical-laboratory variables considered in the statistical analysis. Table S1. Clinical-biochemical-radiological characteristics of patients included in the final data analysis. Table S2. Histopathologic findings. Fig. S1. Flow chart of the patient selection. Fig. S2. Correlation matrix. Fig. S3. Choice of the number of clusters (K). Fig. S4. Distribution of intervals from symptoms onset to hospital admission, from symptoms to dispnoea onset, from symptoms onset to MV start, and from CPAP to MV start among clusters. Fig. S5. Kaplan Meier curves of all analyzed patients. Fig. S6. Kaplan Meier curves of clusters of patients treated with positive pressure ventilation. Fig. S7. Histopathology of a case with aspergillosis

Additional file 2 of Long-term outcome of COVID-19 patients treated with helmet noninvasive ventilation vs. high-flow nasal oxygen: a randomized trial

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Additional file 1 of Long-term outcome of COVID-19 patients treated with helmet noninvasive ventilation vs. high-flow nasal oxygen: a randomized trial

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Additional file 3 of Long-term outcome of COVID-19 patients treated with helmet noninvasive ventilation vs. high-flow nasal oxygen: a randomized trial

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Additional file 4 of Long-term outcome of COVID-19 patients treated with helmet noninvasive ventilation vs. high-flow nasal oxygen: a randomized trial

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Additional file 1 of Dynamics of disease characteristics and clinical management of critically ill COVID-19 patients over the time course of the pandemic: an analysis of the prospective, international, multicentre RISC-19-ICU registry

Additional file 1. Online Supplementary Material