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Background: The Abbreviated Injury Scale (AIS) and Injury Severity Score (ISS) grade the severity of injuries and are useful for trauma audit and benchmarking. However, AIS coding is complex and requires specifically trained staff. A simple yet reliable scoring system is needed. The aim of this study was two-fold. First, to develop and validate a simplified AIS (sAIS) chart centred on the most frequent injuries for use by non-trained healthcare professionals. Second, to evaluate the diagnostic accuracy of the sAIS (index test) to calculate the simplified ISS (sISS) to identify major trauma, compared with the reference AIS (rAIS) to calculate the reference ISS (rISS).
Methods: This retrospective study used data (2013-2014) from the Northern French Alps Trauma Registry to develop and internally validate the sAIS. External validation was performed with data from the Trauma Registry of Acute Care of Lausanne University Hospital, Switzerland (2019-2021). Both datasets comprised a random sample of 100 injured patients. Following the Standards for Reporting of Diagnostic Accuracy Studies 2015 guidelines, all patients completed the rAIS and the sAIS. The sISS and the rISS were calculated using the sAIS and the rAIS, respectively. Accuracy was evaluated with the mean difference between the sISS and the rISS and the Pearson correlation coefficient. A clinically relevant equivalence limit was set at ± 4 ISS points. Precision was analyzed using Bland-Altmann plots with 95% limits of agreement.
Results: Accuracy was good. The mean ISS difference of 0.97 (95% CI, -0.03 to 1.97) in the internal validation dataset and - 1.77 (95% CI, - 3.04 to 0.50) in the external validation dataset remained within the equivalence limit. The Pearson correlation coefficient was 0.93 in the internal validation dataset (95% CI, 0.90-0.95) and 0.82 in the external validation dataset (95% CI, 0.75-0.88). The limits of agreement were wider than the predetermined relevant range.
Conclusions: The sAIS is accurate, but slightly imprecise in calculating the ISS. The development of this scale increases the possibilities to use a scoring system for severely injured patients in settings with a reduced availability of the AIS.
Trial Registration: Retrospectively registered.
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http://dx.doi.org/10.1186/s13049-025-01320-7 | DOI Listing |
J Imaging Inform Med
September 2025
Department of Biomedical Engineering, Gachon University, Seongnam-Si 13120, Gyeonggi-Do, Republic of Korea.
To develop and validate a deep-learning-based algorithm for automatic identification of anatomical landmarks and calculating femoral and tibial version angles (FTT angles) on lower-extremity CT scans. In this IRB-approved, retrospective study, lower-extremity CT scans from 270 adult patients (median age, 69 years; female to male ratio, 235:35) were analyzed. CT data were preprocessed using contrast-limited adaptive histogram equalization and RGB superposition to enhance tissue boundary distinction.
View Article and Find Full Text PDFBehav Res Methods
September 2025
Faculty of Psychology and Cognitive Sciences, Adam Mickiewicz University, Poznań, Poland.
Emotional crying is a complex and multifaceted expression that is frequently observed in humans. Its communicative effects have been recently studied in more detail. However, many studies focus on just one specific feature of emotional crying, most often emotional tears, neglecting the complex nature of the expression.
View Article and Find Full Text PDFImmunol 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.
Pharm Res
September 2025
Axcelead Tokyo West Partners, Inc. Translational Science, Discovery DMPK, Hino-Shi, Tokyo, 191-0065, Japan.
Purpose: Accurate prediction of human clearance (CL) is essential in early drug development. Single Species Scaling (SSS) using rat pharmacokinetic (PK) data, particularly with unbound plasma fraction (f), is widely used. However, its accuracy declines for compounds with extremely low f, and no systematic method has addressed this limitation.
View Article and Find Full Text PDFTheor Appl Genet
September 2025
Institute for Breeding Research on Agricultural Crops, Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Sanitz, 18190, Germany.
Low-cost and high-throughput RNA sequencing data for barley RILs achieved GP performance comparable to or better than traditional SNP array datasets when combined with parental whole-genome sequencing SNP data. The field of genomic selection (GS) is advancing rapidly on many fronts including the utilization of multi-omics datasets with the goal of increasing prediction ability and becoming an integral part of an increasing number of breeding programs ensuring future food security. In this study, we used RNA sequencing (RNA-Seq) data to perform genomic prediction (GP) on three related barley RIL populations.
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