Objective: To determine the frequency and etiology of diagnostic errors during the first 7 days of admission for inborn neonatal intensive care unit (NICU) patients.
Study Design: We conducted a retrospective cohort study of 600 consecutive inborn admissions. A physician used the "Safer Dx NICU Instrument" to review the electronic health record for the first 7 days of admission, and categorized cases as "yes," "unclear," or "no" for diagnostic error. A secondary reviewer evaluated all "yes" charts plus a random sample of charts in the other categories. Subsequently, all secondary reviewers reviewed records with discordance between primary and secondary review to arrive at consensus.
Results: We identified 37 diagnostic errors (6.2% of study patients) with "substantial agreement" between reviewers (κ = 0.66). The most common diagnostic process breakdown was missed maternal history (51%).
Conclusion: The frequency of diagnostic error in inborn NICU patients during the first 7 days of admission is 6.2%.
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http://dx.doi.org/10.1038/s41372-022-01359-9 | DOI Listing |
Codas
January 2025
Programa de Pós-Graduação em Fonoaudiologia, Universidade Estadual Paulista "Júlio de Mesquita Filho" - UNESP - Marília (SP), Brasil.
Purpose: To investigate whether there is a difference in the classification of speech hypernasality by inexperienced listeners using different ordinal scales; to verify the agreement of the listeners in the analyses when using these scales; and to verify whether the order in which the scales are presented influences the results.
Methods: Twenty Speech-Language Pathology students classified the degrees of hypernasality of 40 (oral) samples from patients with cleft lip and palate. Ten performed the classifications using a 4-point scale (absent, mild, moderate, and severe) and, after two weeks, using a 3-point scale (absent, slightly hypernasal, and very hypernasal).
J Clin Immunol
January 2025
Department of Health Systems & Implementation Science, Virginia Tech Carilion School of Medicine, Roanoke, VA, USA.
Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFWorld J Radiol
January 2025
Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06500, Türkiye.
Oral and maxillofacial diagnostic imaging is of paramount importance in dental clinical diagnosis, treatment planning, and follow-up procedures. Periapical radiographic examination and numerous panoramic systems are used in routine clinical dental practice. Cone beam CT is widely used and currently the method of choice in oral and maxillofacial implantology, endodontics, maxillofacial surgery, periodontics, degenerative temporomandibular joint disease, orthodontics, airway studies, sleep disorders, and forensic dentistry.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
This retrospective study evaluated the efficacy of large language models (LLMs) in improving the accuracy of Chinese ultrasound reports. Data from three hospitals (January-April 2024) including 400 reports with 243 errors across six categories were analyzed. Three GPT versions and Claude 3.
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