Objective: We found previously that inappropriate inpatient antimicrobial use was often attributable to erroneous diagnoses. Here, we detail diagnostic errors and their relationship to inappropriate antimicrobial courses.
Design: Retrospective cohort study.
Setting: Veterans Affairs hospital.
Patients: A cohort of 500 randomly selected inpatients with an antimicrobial course.
Methods: Blinded reviewers judged the accuracy of the initial provider diagnosis for the condition that led to an antimicrobial course and whether the course was appropriate. RESULTS The diagnoses were correct in 291 cases (58%), incorrect in 156 cases (31%), and of indeterminate accuracy in 22 cases (4%). In the remaining 31 cases (6%), the diagnosis was a sign or symptom rather than a syndrome or disease. The odds ratio of a correct diagnosis was 4.3 (95% confidence interval [CI], 2.2-8.5) if the index condition was related to the reason for admission. When the diagnosis was correct, 181 of 292 courses (62%) were appropriate, compared with only 10 of 208 (5%) when the diagnosis was incorrect or indeterminate or when providers were treating a sign or symptom rather than a syndrome or disease (P<.001). Among the 309 cases in which antimicrobial courses were not appropriate, reasons varied by diagnostic accuracy; in 81 of 111 cases (73%) with a correct diagnosis, incorrect antimicrobial(s) were selected; in 166 of 198 other cases (84%), antimicrobial therapy was not indicated.
Conclusions: Diagnostic accuracy is important for optimal inpatient antimicrobial use. Antimicrobial stewardship strategies should help providers avoid diagnostic errors and know when antimicrobial therapy can be withheld safely.
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Department of Oral and Maxillofacial Pathology, School of Dentistry, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
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Accurate melanoma diagnosis is crucial for patient outcomes and reliability of AI diagnostic tools. We assess interrater variability among eight expert pathologists reviewing histopathological images and clinical metadata of 792 melanoma-suspicious lesions prospectively collected at eight German hospitals. Moreover, we provide access to the largest panel-validated dataset featuring dermoscopic and histopathological images with metadata.
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Department of Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Facial reconstruction, a crucial method in forensic identification, finds particular significance in cases where conventional means of identification are unavailable. This study addresses a significant gap in the field of forensic facial reconstruction focusing on facial soft tissue thickness (FSTT) and facial reconstruction techniques specifically tailored to the Thai population. By developing and implementing the 3D (three-dimensional) facial reconstruction program and compiling an extensive dataset of FSTT, this research makes substantial progress in advancing forensic facial reconstruction methodologies employing the combination Manchester Method, 3D skull images obtained through cone beam computed tomography (CBCT) scans were reconstructed using Autodesk Maya software.
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