Objectives: The aim of this study was to determine the prevalence and methods of expert knowledge elicitation (EKE) for specifying input parameters in health economic decision models (HEDM).
Methods: We created two samples using the National Health System Economic Evaluations Database: (1) 100 randomly selected HEDM studies to determine prevalence of EKE and (2) sixty studies using a formal EKE process to determine methods used.
Results: Fifty-seven (57 percent) of the random sample included at least one EKE-derived parameter. Of these, six (10 percent) used a formal expert process. Thirty-four studies from our second sample of sixty studies (57 percent) described at least one aspect of the process (e.g., elicitation method) with reasonable clarity. In approximately two-thirds of studies the external experts estimated parameters de novo; the remainder confirmed or modified initial estimates provided by authors, or the method was unclear. The majority of elicitations obtained point estimates only, although a few studies asked experts to estimate ranges of parameter values.
Conclusions: The use of EKE for parameter estimation is common in HEDMs, although there is room for improvement in the methods used.
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http://dx.doi.org/10.1017/S0266462314000427 | DOI Listing |
Cancer Treat Rev
January 2025
Department of Oncology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden. Electronic address:
Importance: Endocrine treatments, such as Tamoxifen (TAM) and/or Aromatase inhibitors (AI), are the adjuvant therapy of choice for hormone-receptor positive breast cancer. These agents are associated with menopausal symptoms, adversely affecting drug compliance. Topical estrogen (TE) has been proposed for symptom management, given its' local application and presumed reduced bioavailability, however its oncological safety remains uncertain.
View Article and Find Full Text PDFPLoS One
January 2025
Nova School of Business and Economics, Universidade Nova de Lisboa, Carcavelos, Portugal.
This empirical study assessed the potential of developing a machine-learning model to identify children and adolescents with poor oral health using only self-reported survey data. Such a model could enable scalable and cost-effective screening and targeted interventions, optimizing limited resources to improve oral health outcomes. To train and test the model, we used data from 2,133 students attending schools in a Portuguese municipality.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA 02139.
The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges.
View Article and Find Full Text PDFJ Clin Lab Anal
January 2025
Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
Background: In the oral environment, the production of bacteriocins or antimicrobial peptides (AMPs) plays a crucial role in maintaining ecological balance by impeding the proliferation of closely related microorganisms. This study aims to conduct in silico genome screening of Streptococcus salivarius to identify potential antimicrobial compounds existing as hypothetical peptides, with the goal of developing novel synthetic antimicrobial peptides.
Methods: Draft genomes of various oral Streptococcus salivarius strains were obtained from the NCBI database and subjected to analysis using bioinformatic tools, viz.
Background: The supply of future registered nurses successfully matriculating from undergraduate nursing programs is critical to address the national nursing shortage. Mentoring in higher education increases recruitment and retention within nursing programs. E-mentoring is an innovative approach to mentorship within nursing education that can optimize undergraduate nursing graduation rates.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!