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https://www.selleckchem.com/CDK.html KEY RESULTS Model performance was measured on the validation dataset. A random forest model-mini serious illness algorithm-used 8 variables from the initial 48 h of hospitalization and predicted death within 6 months with an AUC of 0.92 (0.91-0.93). Red cell distribution width was the most important prognostic variable. min-SIA (mini serious illness algorithm) was very well calibrated and estimated the probability of death to within 10% of the actual value. The discriminative ability of the min-SIA was significantly better than historical estimates of clinician performance. CONCLUSION min-SIA algorithm can identify patients at high risk of 6-month mortality at the time of hospital admission. It can be used to improved access to timely, serious illness care conversations in high-risk patients.BACKGROUND Diabetes Canada launched a comprehensive Dissemination and Implementation (D&I) strategy to optimize uptake of their 2013 Clinical Practice Guidelines; the strategy involved continuing professional development courses, webinars, an interactive website, applications for mobile devices, point-of-care decision support tools, and media awareness campaigns. It included a focus on promoting HbA1c as the recommended diagnostic test for diabetes. OBJECTIVE To determine the impact of Diabetes Canada's 2013 D&I strategy on physician test-ordering behavior, specifically HbA1c testing, for the diagnosis of diabetes, using provincial healthcare administrative data. DESIGN Population-based interrupted time series. SETTING Ontario, Canada. PARTICIPANTS Ontario residents aged 40-79 not previously diagnosed with diabetes. MEASUREMENTS For each quarter between January 2005 and December 2014, we conducted an interrupted time series analysis on the first-order difference of the proportion of patients receiving HbA1c t. Furthermore, differential uptake by user groups suggests that future strategies should include targeted barrier analysis and intervent

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