The fifth revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) was the most controversial in the manual's history. This review selectively surveys some of the most important changes in DSM-5, including structural/organizational changes, modifications of diagnostic criteria, and newly introduced categories. It analyzes why these changes led to such heated controversies, which included objections to the revision's process, its goals, and the content of altered criteria and new categories. The central focus is on disputes concerning the false positives problem of setting a valid boundary between disorder and normal variation. Finally, this review highlights key problems and issues that currently remain unresolved and need to be addressed in the future, including systematically identifying false positive weaknesses in criteria, distinguishing risk from disorder, including context in diagnostic criteria, clarifying how to handle fuzzy boundaries, and improving the guidelines for "other specified" diagnosis.
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http://dx.doi.org/10.1146/annurev-clinpsy-032814-112800 | DOI Listing |
BMJ Evid Based Med
December 2024
Department of Public Health, History of Science, and Gynecology, Miguel Hernandez University of Elche Faculty of Medicine, Sant Joan D'Alacant, Comunidad Valenciana, Spain
Objective: The objective of this study is to analyse the perspectives of screening candidates and healthcare professionals on shared decision-making (SDM) in prostate cancer (PCa) screening using the prostate-specific antigen (PSA) test.
Design: Descriptive qualitative study (May-December 2022): six face-to-face focus groups and four semistructured interviews were conducted, transcribed verbatim and thematically analysed using ATLAS.ti software.
Clin Chem
January 2025
Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States.
Background: Multianalyte machine learning (ML) models can potentially identify previously undetectable wrong blood in tube (WBIT) errors, improving upon current single-analyte delta check methodology. However, WBIT detection model performance has not been assessed in a real-world, low-prevalence context. To estimate real-world positive predictive values, we propose a methodology to assess WBIT detection models by evaluating the impact of missing data and by using a "low prevalence" validation data set.
View Article and Find Full Text PDFJ Clin Med
January 2025
Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy.
Sepsis is one of the leading causes of mortality in hospital settings, and early diagnosis is a crucial challenge to improve clinical outcomes. Artificial intelligence (AI) is emerging as a valuable resource to address this challenge, with numerous investigations exploring its application to predict and diagnose sepsis early, as well as personalizing its treatment. Machine learning (ML) models are able to use clinical data collected from hospital Electronic Health Records or continuous monitoring to predict patients at risk of sepsis hours before the onset of symptoms.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China.
The Loess Plateau in northwest China features fragmented terrain and is prone to landslides. However, the complex environment of the Loess Plateau, combined with the inherent limitations of convolutional neural networks (CNNs), often results in false positives and missed detection for deep learning models based on CNNs when identifying landslides from high-resolution remote sensing images. To deal with this challenge, our research introduced a CNN-transformer hybrid network.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain.
Conducting penetration testing (pentesting) in cybersecurity is a crucial turning point for identifying vulnerabilities within the framework of Information Technology (IT), where real malicious offensive behavior is simulated to identify potential weaknesses and strengthen preventive controls. Given the complexity of the tests, time constraints, and the specialized level of expertise required for pentesting, analysis and exploitation tools are commonly used. Although useful, these tools often introduce uncertainty in findings, resulting in high rates of false positives.
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