Background And Objectives: To investigate if the Injury Severity Score (ISS) and the Abbreviated Injury Score (AIS) are correlated with the long-term quality of life in severe trauma patients.
Methods: Patients injured from 2005 to 2007 with an ISS≥15 were surveyed 16-24 months after injury. The Health Assessment Questionnaire (HAQ-DI) was used for measuring the functional status and the Short Form-12 (SF-12) was used for measuring the health status divided into its two components, the PCS (Physical Component Summary) and the MCS (Mental Component Summary). The results of the questionnaires were compared with the ISS and AIS components. Results of the SF-12 were compared with the values expected from the general population.
Results: Seventy-four patients filled the questionnaires (response rate 28%). The mean scores were: PCS 42.6±13.3; MCS 49.4±1.4; HAQ-DI 0.5±0.7. Correlation was observed with the HAQ-DI and the PCS (Spearman's Rho: -0.83; p<0.05) and no correlation between the HAQ-DI and the MCS neither between the MCS and PCS (Spearman's Rho=-0.21; and 0.01 respectively). The cutaneous-external and extremities-pelvic AIS punctuation were correlated with The PCS (Spearman's Rho: -0.39 and -0.34, p<0.05) and with the HAQ-DI (Spearman's Rho: 0.31 and 0.23; p<0.05). The physical condition compared with the regular population was worse except for the groups aged between 65-74 and 55-64.
Conclusions: Patients with extremities and pelvic fractures are more likely to suffer long-term disability. The severity of the external injuries influenced the long-term disability.
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http://dx.doi.org/10.1016/j.bjane.2013.03.008 | DOI Listing |
Sci Rep
December 2024
Institute for Forest Resources and Environment of Guizhou, College of Forestry, Guizhou University, Guiyang, 550025, Guizhou, China.
This study aims to explore the low phosphorus (P) tolerance of saplings from different Gleditsia sinensis Lam. families. It also seeks to screen for Gleditsia sinensis families with strong low P tolerance and identify key indicators for evaluating their tolerance.
View Article and Find Full Text PDFNat Commun
December 2024
Computational Neuroscience Unit, Intelligent Systems Labs, Faculty of Engineering, University of Bristol, Bristol, UK.
The brain must maintain a stable world model while rapidly adapting to the environment, but the underlying mechanisms are not known. Here, we posit that cortico-cerebellar loops play a key role in this process. We introduce a computational model of cerebellar networks that learn to drive cortical networks with task-outcome predictions.
View Article and Find Full Text PDFPerspect Med Educ
December 2024
University of California, San Francisco, US.
When health professions learners do not meet standards on assessments, educators need to share this information with the learners and determine next steps to improve their performance. Those conversations can be difficult, and educators may lack confidence or skill in holding them. For clinician-educators with experience sharing challenging news with patients, using an analogy from clinical settings may help with these conversations in the education context.
View Article and Find Full Text PDFOnco Targets Ther
December 2024
Department of Thoracic Surgery, West China Hospital of Sichuan University, Chengdu, People's Republic of China.
Background: This study investigates the prognostic value of M0 macrophage-related genes (M0MRGs) in esophageal cancer (ESCA) and identifies novel targets for immunotherapy.
Methods: Differentially expressed genes (DEGs) were screened with ESCA-related expression profile data (GSE5364 and GSE17351) from the GEO database, followed by GO and KEGG pathway enrichment analyses. Then, immune cell infiltration was examined with the CIBERSORT algorithm and multiplex fluorescence-based immunohistochemistry (MP-IHC).
Front Physiol
December 2024
Department of Oral & Maxillofacial Surgery, Shenzhen Stomatology Hospital, Affiliated to Shenzhen University, Shenzhen, Guangdong Province, China.
Introduction: This study aimed to develop a deep learning-based method for interpreting magnetic resonance imaging (MRI) scans of temporomandibular joint (TMJ) anterior disc displacement (ADD) and to formulate an automated diagnostic system for clinical practice.
Methods: The deep learning models were utilized to identify regions of interest (ROI), segment TMJ structures including the articular disc, condyle, glenoid fossa, and articular tubercle, and classify TMJ ADD. The models employed Grad-CAM heatmaps and segmentation annotation diagrams for visual diagnostic predictions and were deployed for clinical application.
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