Journal of evaluation in clinical practice
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Myalgic encephalomyelitis (ME, also known as chronic fatigue syndrome or ME/CFS) is a debilitating, complex, multisystem illness. Developing a comprehensive understanding of the multiple and interconnected barriers to optimal care will help advance strategies and care models to improve quality of life for people living with ME in Canada. ⋯ People living with ME in Canada face significant barriers to care, though this has received relatively limited attention. This synthesis, which points to several areas for future research, can be used as a starting point for researchers, healthcare providers and decision-makers who are new to the area or encountering ME more frequently due to the COVID-19 pandemic.
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We aimed to demonstrate the use of quantitative bias analysis (QBA), which reveals the effects of systematic error, including confounding, misclassification and selection bias, on study results in epidemiological studies published in the period from 2010 to mid-23. ⋯ The application of QBA is rare in the literature but is increasing over time. Future researchers should include detailed analyzes such as QBA analysis to obtain inferences with higher evidence value, taking into account systematic errors.
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The present paper aimed at discussing how the process of decision-making should be taken care of in healthcare services. ⋯ In depht analysis of meaning-making processes is crucial for better refining good practices of shared decision-making.
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Healthcare systems remain disease oriented despite growing sustainability concerns caused by inadequate management of patients with multimorbidity. Comprehensive care programmes (CCPs) can play an important role in streamlining care delivery, but large differences in setup and results hinder firm conclusions on their effectiveness. Many elements for successful implementation of CCPs are identified, but strategies to overcome barriers and embed programmes within health systems remain unknown. ⋯ The introduction of a CCP is feasible, and exploratory analysis on effectiveness shows lower hospital care use without decreasing patient satisfaction. However, this is accompanied by several challenges that show current fragmented systems still do not support implementation of integrated care initiatives. Overcoming those comes with substantial costs and may require a strong bottom-up implementation within a motivated team and actions on all levels of healthcare systems.
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Clinical abbreviations pose a challenge for clinical decision support systems due to their ambiguity. Additionally, clinical datasets often suffer from class imbalance, hindering the classification of such data. This imbalance leads to classifiers with low accuracy and high error rates. Traditional feature-engineered models struggle with this task, and class imbalance is a known factor that reduces the performance of neural network techniques. ⋯ Deep neural network methods, particularly Bi-LSTM, offer promising alternatives to traditional feature-engineered models for clinical abbreviation disambiguation. By employing data generation techniques, we can address the challenges posed by limited-resource and imbalanced clinical datasets. This approach leads to a significant improvement in model accuracy for clinical abbreviation disambiguation tasks.