Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Objectives: Autopsy plays an essential role in detecting diagnostic errors and the findings from autopsies have the potential to reduce future errors. However, there are few reports from Japan on diagnostic errors based on autopsy diagnoses. This study aimed to detail diagnostic errors in autopsy reports in Japan.
Methods: This descriptive study utilized the case report abstract database of the Japanese Society of Internal Medicine chapter meetings. Autopsy cases from 2002 to 2022 were included. We defined diagnostic errors as discrepancies in the primary cause of death between autopsy and clinical diagnosis. Diagnostic error cases were also categorized according to the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10). To observe trends, a chi-square test was conducted by dividing the 20 years of data into four groups.
Results: Among 1,213 autopsied cases, diagnostic errors occurred in 435 cases (35.9 %; 95 % confidence interval, 33.2-38.6 %). The most frequent category of autopsy-detected diagnostic error cases was neoplasms (147, 33.8 %), followed by infections (131, 30.1 %), and cardiovascular diseases (49, 11.3 %). Over the 20 years, the incidence of diagnostic errors neither increased nor decreased.
Conclusions: Diagnostic errors detected in 35.8 % of autopsy cases in Japan. Autopsy is an important quality indicator for identifying diagnostic error.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1515/dx-2025-0013 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Multi-class machine learning-based classification of SCID-related genetic variants.
Immunol Res
September 2025
Department of Immunology and Allergy, Faculty of Medicine, Necmettin Erbakan University, Konya, Türkiye.
Background: Variants of uncertain significance (VUS) represent a major diagnostic challenge in the interpretation of genetic testing results, particularly in the context of inborn errors of immunity such as severe combined immunodeficiency (SCID). The inconsistency among computational prediction tools often necessitates expensive and time-consuming wet-lab analyses.
Objective: This study aimed to develop disease-specific, multi-class machine learning models using in silico scores to classify SCID-associated genetic variants and improve the interpretation of VUS.
INTEROBSERVER AGREEMENT OF INTRAPAPILLARY CAPILLARY LOOPS CLASSIFICATION FOR SUPERFICIAL ESOPHAGEAL SQUAMOUS CELL CARCINOMA IN A WESTERN CENTER.
Arq Gastroenterol
September 2025
Faculdade de Medicina da Universidade de São Paulo, Departamento de Gastroenterologia, São Paulo, SP, Brasil.
Background: Accurate evaluation of the invasion depth of superficial esophageal squamous cell carcinoma (SESCC) is crucial for optimal treatment. While magnifying endoscopy (ME) using the Japanese Esophageal Society (JES) classification is reported as the most accurate method to predict invasion depth, its efficacy has not been tested in the Western world. This study aims to evaluate the interobserver agreement of the JES classification for SESCC and its accuracy in estimating invasion depth in a Brazilian tertiary hospital.
View Article and Find Full Text PDFAn Explainable Deep Learning Model for Focal Liver Lesion Diagnosis Using Multiparametric MRI.
Radiol Artif Intell
September 2025
Department of Radiology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, No. 197 Ruijin Er Road, Shanghai 200025, China.
Purpose To assess the effectiveness of an explainable deep learning (DL) model, developed using multiparametric MRI (mpMRI) features, in improving diagnostic accuracy and efficiency of radiologists for classification of focal liver lesions (FLLs). Materials and Methods FLLs ≥ 1 cm in diameter at mpMRI were included in the study. nn-Unet and Liver Imaging Feature Transformer (LIFT) models were developed using retrospective data from one hospital (January 2018-August 2023).
View Article and Find Full Text PDFAmmonia-to-Urea Ratio: A Noninvasive First-Line Tool for Detecting Clinically Significant Portal Hypertension.
JGH Open
September 2025
Department of Genomic Medicine, Division of Biochemistry, Molecular Biology, and Nutrition University Hospital of Nancy Nancy France.
Introduction: Cirrhosis progresses from compensated to decompensated phases, often marked by portal hypertension and complications like ascites, variceal hemorrhage, and hepatic encephalopathy. The ammonia-to-urea (A-to-U) ratio, reflecting urea cycle efficiency, may offer superior diagnostic performance compared to plasma ammonia levels alone. This study compared the diagnostic accuracy of the A-to-U ratio and plasma ammonia levels for identifying portal hypertension.
View Article and Find Full Text PDFLung volume segmentation in fetal MRI: super-resolution reconstructions improve inter-rater reliability.
Eur Radiol Exp
September 2025
Center for MR-Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.
Background: Fetal MRI is increasingly used to investigate fetal lung pathologies, and super-resolution (SR) algorithms could be a powerful clinical tool for this assessment. Our goal was to investigate whether SR reconstructions result in an improved agreement in lung volume measurements determined by different raters, also known as inter-rater reliability.
Materials And Methods: In this single-center retrospective study, fetal lung volumes calculated from both SR reconstructions and the original images were analyzed.