It is often useful to conduct inference for probability densities by constructing "plausible" sets in which the unknown density of given data may lie. Examples of such sets include pointwise intervals, simultaneous bands, or balls in a function space, and they may be frequentist or Bayesian in interpretation. For almost any density estimator, there are multiple approaches to inference available in the literature. Here we review such literature, providing a thorough overview of existing methods for density uncertainty quantification. The literature considered here comprises a spectrum from theoretical to practical ideas, and for some methods there is little commonality between these two extremes. After detailing some of the key concepts of nonparametric inference -the different types of "plausible" sets, and their interpretation and behaviour -we list the most prominent density estimators and the corresponding uncertainty quantification methods for each.
XForms is a cross device, host-language independent markup language for declaratively defining a data processing model of XML data and its User Interface. It reduces the amount of markup that has to be written for creating rich webapplications dramatically. There is no need to write any code to keep the UI in sync with the model, this is completely handled by the XForms processor. XForms 2.0 is the next huge step forward for XForms, making it an easy to use framework for creating powerful web applications. This paper will highlight the power of these new features, and show how they can be used to create real life web-applications. It will also discuss a possible framework for building custom components, which is currently still missing in XForms.
Background: Despite evidence of their benefits, decision aids (DAs) have not been widely adopted in clinical practice. Quality improvement methods could help embed DA delivery into primary care workflows and facilitate DA delivery and uptake, defined as reading or watching DA materials. Objectives: 1) Work with clinic staff and providers to develop and test multiple processes for DA delivery; 2) implement a systems approach to measuring delivery and uptake; 3) compare uptake and patient satisfaction across delivery models. Methods: We employed a microsystems approach to implement three DA delivery models into primary care processes and workflows: within existing disease management programs, by physician request, and by mail. We developed a database and tracking tools linked to our electronic health record and designed clinic-based processes to measure uptake and satisfaction. Results: A total of 1144 DAs were delivered. Depending on delivery method, 51% to 73% of patients returned to the clinic within 6 months. Nurses asked 67% to 75% of this group follow-up questions, and 65% to 79% recalled receiving the DA. Among them, uptake was 23% to 27%. Satisfaction among patients who recalled receiving the DA was high. Eighty-two to 93% of patients reported that they liked receiving this patient education information, and 82% to 91% reported that receiving patient education information like this is useful to them. Conclusion: Our results demonstrate the realities of clinical practice. One fourth to one third of patients did not return for a follow-up visit. Although nurses were able to assess uptake in the course of their usual duties, the results did not achieve the standards typically expected of clinical research. Despite these limitations, uptake, though modest, was similar across delivery methods, suggesting that there are multiple strategies for implementing DAs in clinical practice.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.