Objectives: The aim of this study was to identify the relationship and impact between Real World Evidence (RWE) and experimental evidence (EE) in Polish decision-making processes for the drugs from selected Anatomical Therapeutic Chemical (ATC) groups.
Study Design: Descriptive study.
Methods: A detailed analysis was performed for 58 processes from five ATC code groups in which RWE for effectiveness, or effectiveness and safety were cited in Agency for Health Technology Assessment and Tariff System's (AOTMiT) documents published between January 2012 and September 2015: Verification Analysis of AOTMiT, Statement of the Transparency Council of AOTMiT, and Recommendation of the President of AOTMiT.
Results: In 62% of the cases, RWE supported the EE and confirmed its main conclusions. The majority of studies in the EE group showed to be RCTs (97%), and the RWE group included mainly cohort studies (89%). There were more studies without a control group within RWE compared with the EE group (10% vs 1%). Our results showed that EE are more often assessed using Jadad, NICE or NOS scale by AOTMiT compared with RWE (93% vs 48%). When the best evidence within a given decision-making process is analysed, half of RWE and two-thirds of EE are considered high quality evidence.
Conclusions: RWE plays an important role in the decision-making processes on public funding of drugs in Poland, contributing to nearly half (45%) of all the evidence considered. There exist such processes in which the proportion of RWE is dominant, with one process showing RWE as the only evidence presented.
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http://dx.doi.org/10.1016/j.puhe.2016.12.025 | DOI Listing |
Sports Health
January 2025
University of Bradford, Bradford, UK.
Risk factors associated with depression in athletes include biological sex, physical pain, and history of sport-related concussion (SRC). However, although there are well-documented benefits of sport and physical activity on mental health, many sportspeople still take the risk of competing in contact sports. Therefore, this infographic, supported by scientific evidence, aims to provide sportspeople with an informed decision on their participation.
View Article and Find Full Text PDFActa Neuropathol Commun
January 2025
Department of Physiology and Pharmacology, Sapienza University of Rome, 00185, Rome, Italy.
The generation of retinal models from human induced pluripotent stem cells holds significant potential for advancing our understanding of retinal development, neurodegeneration, and the in vitro modeling of neurodegenerative disorders. The retina, as an accessible part of the central nervous system, offers a unique window into these processes, making it invaluable for both study and early diagnosis. This study investigates the impact of the Frontotemporal Dementia-linked IVS 10 + 16 MAPT mutation on retinal development and function using 2D and 3D retinal models derived from human induced pluripotent stem cells.
View Article and Find Full Text PDFBMC Ophthalmol
January 2025
Ophthalmology Unit, Queen Margaret Hospital, NHS Fife, Dunfermline, UK.
Background: COVID-19 caused a huge backlog of patients in glaucoma clinics. This study describes redesign of an entire glaucoma service with electronic patient triage to three levels and utilisation of the Scottish optometry infrastructure of upskilled optometrists.
Methods: 2276 patients in glaucoma clinics were identified and triaged to three levels in keeping with Glauc-strat-fast guidance with local amendments.
Sci Rep
January 2025
Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, UK.
In general, edge computing networks are based on a distributed computing environment and hence, present some difficulties to obtain an appropriate load balancing, especially under dynamic workload and limited resources. The conventional approaches of Load balancing like Round-Robin and Threshold-based load balancing fails in scalability and flexibility issues when applied to highly variable edge environments. To solve the problem of how to achieve steady-state load balance and provide dynamic adaption to edge networks, this paper proposes a new framework that using PCA and MDP.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progression for effective personalized care. However, AI's imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human-AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma.
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