Background: The paper sets out the development, validity, and responsiveness of the Integrative Medicine Treatment Evaluation Form (IMTEF), which has been designed to measure the effects of complementary and integrative therapy (CIT) interventions in cancer and palliative care (PC) patients in a National Health Service (NHS) hospital setting. Treatment evaluation is essential for ensuring safety and quality of services, for meeting NHS governance requirements. It also helps to add to the evidence base for complementary and integrative therapies through collecting data about treatments.
Methods: A number of different Patient Reported Outcome Measures (PROMs) tools were reviewed in order to design the IMTEF, which details questions that captures both quantitative and qualitative data. The IMTEF was reviewed by patients and a range of health care practitioners.
Results: IMTEF's validity is supported by feedback from health care practitioners and patients, by its ability to detect different degrees of change in relation to change scores, and by its correlations with Visual Analog Scale (VAS) scores.
Conclusion: The IMTEF can be used to assess the effects of therapeutic bodywork and CITs when many of the patients do not have the capacity or the time to answer many questions, and when therapists do not know in advance the number of treatments that patients will be able to receive. Because of the way it is structured, it can also assess the effects after a number of sessions.
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http://dx.doi.org/10.3822/ijtmb.v16i3.859 | DOI Listing |
Sci Rep
December 2024
Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.
The intelligent identification of wear particles in ferrography is a critical bottleneck that hampers the development and widespread adoption of ferrography technology. To address challenges such as false detection, missed detection of small wear particles, difficulty in distinguishing overlapping and similar abrasions, and handling complex image backgrounds, this paper proposes an algorithm called TCBGY-Net for detecting wear particles in ferrography images. The proposed TCBGY-Net uses YOLOv5s as the backbone network, which is enhanced with several advanced modules to improve detection performance.
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December 2024
Department of Production Engineering, KTH Royal Institute of Technology, 11428, Stockholm, Sweden.
This study investigates the implementation of collaborative route planning between trucks and drones within rural logistics to improve distribution efficiency and service quality. The paper commences with an analysis of the unique characteristics and challenges inherent in rural logistics, emphasizing the limitations of traditional methods while highlighting the advantages of integrating truck and drone technologies. It proceeds to review the current state of development for these two technologies and presents case studies that illustrate their application in rural logistics.
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December 2024
Department of Chemistry, University of Washington, Box 351700, Seattle, Washington, 98195, USA.
Trigger valves are fundamental features in capillary-driven microfluidic systems that stop fluid at an abrupt geometric expansion and release fluid when there is flow in an orthogonal channel connected to the valve. The concept was originally demonstrated in closed-channel capillary circuits. We show here that trigger valves can be successfully implemented in open channels.
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December 2024
School of Physical Education, Shanghai University of Sport, Shanghai, 200438, China.
Objective: This study aimed to examine the levels of physical activity (PA), sleep, and mental health (MH), specifically depression, anxiety, and stress, among Chinese university students. It also aimed to analyze the influencing factors of MH, providing a theoretical foundation for developing intervention programs to improve college students' mental health.
Methods: A stratified, clustered, and phased sampling method was employed.
Sci Rep
December 2024
Department of Computer Science, Birzeit University, P.O. Box 14, Birzeit, West Bank, Palestine.
Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming.
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