Introduction: The SCAE-SM (Request for an Appointment in Specialized Care-Suspicion of Malignancy) computer application is a tool available to Primary Care (PC) physicians for the referral of patients who should be evaluated by the specialist in a maximum period of 2 weeks when malignancy is suspected. The objective of our work was to analyze the usefulness of this tool and propose areas for improvement in the management of patients with suspected musculoskeletal malignancy.
Material And Methods: A descriptive cross-sectional study of 235 referrals received in the years 2012-2017 was carried out. Their origin, the information contained in the applications and the response provided by historical evaluators, without specific oncology training, were analyzed. For this study, a new blind assessment of all applications was carried out by 13 orthopedists with different levels of specific training in musculoskeletal oncology (re-evaluators).
Results: Among all SCAE-SM, only 8.23% of patients had aggressive benign or malignant disease. The most successful re-evaluators in the adequacy of early appointment were those with moderate oncological training (5-10 years of experience). During the study, of all the patients treated in the Tumor Unit, only 18.81% accessed through the SCAE-SM circuit, with a mean waiting time of 18.11 days from the PC referral.
Conclusions: The SCAE-SM computer application as tool for improve the management and advance care for patients with malignant musculoskeletal tumor pathology is useful, although the use of the circuit is inadequate. It is necessary to disseminate and generalize it, as well as to implement basic oncology training programs both in the field of PC and Hospital.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.recot.2021.05.006 | DOI Listing |
Alzheimers Res Ther
January 2025
Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills.
View Article and Find Full Text PDFNMR Biomed
March 2025
Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland, Australia.
In this work, we introduce spatial and chemical saturation options for artefact reduction in magnetic resonance fingerprinting (MRF) and assess their impact on T and T mapping accuracy. An existing radial MRF pulse sequence was modified to enable spatial and chemical saturation. Phantom experiments were performed to demonstrate flow artefact reduction and evaluate the accuracy of the T and T maps.
View Article and Find Full Text PDFWorld J Microbiol Biotechnol
January 2025
Graduate Program in Bioscience Technologies, Universidade Tecnológica Federal do Paraná, Toledo, Paraná, Brazil.
Efficient degradation of lignocellulosic biomass is key for the production of value-added products, contributing to sustainable and renewable solutions. This study employs a two-step approach to evaluate lignocellulolytic enzymes of Ceratocystis paradoxa, Colletotrichum falcatum, and Sporisorium scitamineum. First, an in silico genomic analysis was conducted to predict the potential enzyme groups produced by these fungi.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611002, Tamil Nadu, India.
In response to the pressing need for the detection of Monkeypox caused by the Monkeypox virus (MPXV), this study introduces the Enhanced Spatial-Awareness Capsule Network (ESACN), a Capsule Network architecture designed for the precise multi-class classification of dermatological images. Addressing the shortcomings of traditional Machine Learning and Deep Learning models, our ESACN model utilizes the dynamic routing and spatial hierarchy capabilities of CapsNets to differentiate complex patterns such as those seen in monkeypox, chickenpox, measles, and normal skin presentations. CapsNets' inherent ability to recognize and process crucial spatial relationships within images outperforms conventional CNNs, particularly in tasks that require the distinction of visually similar classes.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Science and Engineering, Engineering College Ajmer, Ajmer, Rajasthan, India.
To combat dynamically loaded code in anti-emulated environments, DLCDroid is an Android app analysis framework. DL-CDroid uses the reflection API to effectively identify information leaks due to dynamically loaded code within malicious apps, incorporating static and dynamic analysis techniques. The Dynamically Loaded Code (DLC) technique employs Java features to allow Android apps to dynamically expand their functionality at runtime.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!