Injury
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Meta-analyses constitute fundamental tools of the Evidence-Based Medicine (EBM) aiming at synthesizing outcome data from individual trials in order to produce pooled effect estimates for various outcomes of interest. Combining summary data from several studies increases the sample size, improves the statistical power of the findings as well as the precision of the obtained effect estimates. ⋯ In addition, over the course of the evolution of the current meta-analytic methodology, some concerns have been expressed on the ultimate usefulness of such a complex and time consuming procedure on establishing timely, valid evidence on various specified topics in the field of Orthopaedics and Trauma Surgery. This article provides an overview of the appropriate methodology, benefits and potential drawbacks of the meta-analytic procedure.
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The adoption of evidence-based orthopaedics has shifted the focus from expert base opinions and anecdotal evidence to a focus on integrating the best available clinical research. This shift has led to an increased focus on randomized controlled trials (RCTs) within the field. Although RCTs are considered the highest level of evidence, methodologic errors can introduce bias and limit the validity of the results. Early trials were hampered by lack of blinding, inadequate sample sizes and other design flaws. The objective of this review was to examine the current literature to determine if the design and execution of RCTs has improved. ⋯ Although marked improvements have been made in the design and implementation of trials, there is still considerable room for improvement. Adequately blinded and powered studies evaluating clinically important outcomes and differences should be key considerations in trial design moving forward.
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Artificial intelligence (AI) is a broad term referring to the application of computational algorithms that can analyze large data sets to classify, predict, or gain useful conclusions. Under the umbrella of AI is machine learning (ML). ML is the process of building or learning statistical models using previously observed real world data to predict outcomes, or categorize observations based on 'training' provided by humans. ⋯ AI and ML are becoming cornerstones in the medical and healthcare-research domains and are integral in our continued processing and capitalization of robust patient EMR data. Considerations for the use and application of ML in healthcare settings include assessing the quality of data inputs and decision-making that serve as the foundations of the ML model, ensuring the end-product is interpretable, transparent, and ethical concerns are considered throughout the development process. The current and future applications of ML include improving the quality and quantity of data collected from EMRs to improve registry data, utilizing these robust datasets to improve and standardized research protocols and outcomes, clinical decision-making applications, natural language processing and improving the fundamentals of value-based care, to name only a few.
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Patient-reported outcomes (PROs) capture data related to patients' perception of their health status and aspects of health care delivery. In parallel, digital innovations have advanced the administration, storage, processing, and accessibility of PROs, allowing these data to become actively incorporated in day-to-day clinical practice along the entire patient care pathway. ⋯ This technology-enabled, data-driven approach provides insights which, when actioned, can enhance musculoskeletal care of patients and populations, while enriching the clinician-patient experience of decision making. In this review, we provide an overview of the opportunities enabled by PROs and technology for the cycle of orthopedic care.