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Cognitive Behavioural Therapy to Optimize Post-Operative Recovery (COPE): a randomized controlled feasibility trial in extremity fracture patients.
Pilot Feasibility Stud
January 2025
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Article Synopsis
- The study aimed to assess the feasibility of an online cognitive behavioral therapy (CBT) program for patients recovering from extremity fractures compared to usual care.
- Despite enrolling 94 participants, recruitment showed a "yellow light," indicating some success but needing improvements.
- Compliance with the CBT program was low, as only 60% completed all modules, leading to a "red light," suggesting significant changes are necessary before a larger trial can proceed.
Addressing evidence needs during health crises in the province of Quebec (Canada): a proposed action plan for rapid evidence synthesis.
BMC Health Serv Res
January 2025
Department of School and Social Adaptation Studies, Faculty of Education, Université de Sherbrooke, Sherbrooke, Canada.
Background: The COVID-19 pandemic necessitated the rapid availability of evidence to respond in a timely manner to the needs of practice settings and decision-makers in health and social services. Now that the pandemic is over, it is time to put in place actions to improve the capacity of systems to meet knowledge needs in a situation of crisis. The main objective of this project was thus to develop an action plan for the rapid syntheses of evidence in times of health crisis in Quebec (Canada).
View Article and Find Full Text PDFBayesian network for predicting mandibular third molar extraction difficulty.
BMC Oral Health
January 2025
Sub-Institute of Public Safety Standardization, China National Institute of Standardization, No.4 Zhichun Road, Haidian District, Beijing, 100191, PR China.
Background: This study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results based on the patient's personal situation; and (3) identify key surgical points and propose surgical protocols to decrease complications.
Methods: Relevant articles were searched to identify risk factors. Clinical knowledge and experience were used to analyse the risk factors to establish the Bayesian network.
A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning.
BMC Geriatr
January 2025
Department of Cardiology, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Lanzhou, 730000, China.
Objective: Constructing a predictive model for the occurrence of heart disease in elderly hypertensive individuals, aiming to provide early risk identification.
Methods: A total of 934 participants aged 60 and above from the China Health and Retirement Longitudinal Study with a 7-year follow-up (2011-2018) were included. Machine learning methods (logistic regression, XGBoost, DNN) were employed to build a model predicting heart disease risk in hypertensive patients.
Background: Outpatient training for resident physicians has been attracting attention in recent years. However, to our knowledge, there have only been a few surveys on outpatient training, particularly in Japan. This study evaluates outpatient care among Japanese resident physicians by determining how the volume of outpatient encounters and length of outpatient training correlate with residents' clinical competence.
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