Aim: To estimate and compare sex-specific screening polypectomy rates to quality benchmarks of 40% in men and 30% in women.
Methods: A prospective cohort study was undertaken of patients aged 50-75, scheduled for colonoscopy, and covered by the Québec universal health insurance plan. Endoscopist and patient questionnaires were used to obtain screening and non-screening colonoscopy indications. Patient self-report was used to obtain history of gastrointestinal conditions/symptoms and prior colonoscopy. Sex-specific polypectomy rates (PRs) and 95%CI were calculated using Bayesian hierarchical logistic regression.
Results: In total, 45 endoscopists and 2134 (mean age = 61, 50% female) of their patients participated. According to patients, screening PRs in males and females were 32.4% (95%CI: 23.8-41.8) and 19.4% (95%CI: 13.1-25.4), respectively. According to endoscopists, screening PRs in males and females were 30.2% (95%CI: 27.0-41.9) and 16.6% (95%CI: 16.3-28.6), respectively. Sex-specific PRs did not meet quality benchmarks at all ages except for: males aged 65-69 (patient screening indication), and males aged 70-74 (endoscopist screening indication). For all patients aged 50-54, none of the CI included the quality benchmarks.
Conclusion: Most sex-specific screening PRs in Québec were below quality benchmarks; PRs were especially low for all 50-54 year olds.
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http://dx.doi.org/10.3748/wjg.v20.i43.16300 | DOI Listing |
EClinicalMedicine
August 2024
Division of Cancer Prevention and Population Sciences, Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Background: Lung cancer screening recommendations employ annual frequency for eligible individuals, despite evidence that it may not be universally optimal. The impact of imposing a structure on the screening frequency remains unknown. The ENGAGE framework, a validated framework that offers fully dynamic, analytically optimal, personalised lung cancer screening recommendations, could be used to assess the impact of screening structure on the effectiveness and efficiency of lung cancer screening.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
Division of Software, Yonsei University, Mirae Campus, Yeonsedae-gil 1, Wonju-si, 26493 Gangwon-do Korea.
Purpose: Drug repositioning, a strategy that repurposes already-approved drugs for novel therapeutic applications, provides a faster and more cost-effective alternative to traditional drug discovery. Network-based models have been adopted by many computational methodologies, especially those that use graph neural networks to predict drug-disease associations. However, these techniques frequently overlook the quality of the input network, which is a critical factor for achieving accurate predictions.
View Article and Find Full Text PDFWhile novel deep learning and statistics-based techniques predict accurate structural models for proteins and non-coding RNA, describing their macromolecular conformations in solution is still challenging. Small-angle X-ray scattering (SAXS) in solution is an efficient technique to validate structural predictions by comparing the experimental SAXS profile with those calculated from predicted structures. There are two main challenges in comparing SAXS profiles to RNA structures: the structures often lack cations necessary for stability and charge neutralization, and a single structure inadequately represents the conformational plasticity of RNA.
View Article and Find Full Text PDFBackground: Self-directed interventions are cost-effective for patients with cancer and their family caregivers, but barriers to use can compromise adherence and efficacy.
Aim: Pilot a Sequential Multiple Assignment Randomized Trial (SMART) to develop a time-varying dyadic self-management intervention that follows a stepped-care approach in providing different types of guidance to optimize the delivery of Coping-Together, a dyadic self-directed self-management intervention.
Methods: 48 patients with cancer and their caregivers were randomized in Stage 1 to: (a) Coping-Together (included a workbook and 6 booklets) or (b) Coping-Together + lay telephone guidance.
J Gen Intern Med
January 2025
VA Palo Alto Cooperative Studies Program Coordinating Center, Palo Alto, CA, USA.
Background: Advances in artificial intelligence and machine learning have facilitated the creation of mortality prediction models which are increasingly used to assess quality of care and inform clinical practice. One open question is whether a hospital should utilize a mortality model trained from a diverse nationwide dataset or use a model developed primarily from their local hospital data.
Objective: To compare performance of a single-hospital, 30-day all-cause mortality model against an established national benchmark on the task of mortality prediction.
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