Risk Assessment for Cardiovascular Disease With Nontraditional Risk Factors
US Preventive Services Task Force; Susan J. Curry; Alex H. Krist; Douglas K Owens; Michael J. Barry; Aaron B. Caughey; Karina W. Davidson; Chyke A. Doubeni · 2018 · JAMA
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Importance: Cardiovascular disease (CVD) is the most common cause of death among adults in the United States. Treatment to prevent CVD events by modifying risk factors is currently informed by the Framingham Risk Score, the Pooled Cohort Equations, or similar CVD risk assessment models. If current CVD risk assessment models could be improved by adding more risk factors, treatment might be better targeted, thereby maximizing the benefits and minimizing the harms. Objective: To update the 2009 US Preventive Services Task Force (USPSTF) recommendation on using nontraditional risk factors in coron
Abstract by US Preventive Services Task Force; Susan J. Curry; Alex H. Krist; Douglas K Owens; Michael J. Barry; Aaron B. Caughey; Karina W. Davidson; Chyke A. Doubeni, JAMA (2018) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
A Randomized Trial of Intraarterial Treatment for Acute Ischemic Stroke
Negative / Null Result ReportDuodenal Infusion of Donor Feces for Recurrent Clostridium difficile
Negative / Null Result ReportStenting versus Endarterectomy for Treatment of Carotid-Artery Stenosis
Negative / Null Result ReportEffects of Combination Lipid Therapy in Type 2 Diabetes Mellitus
Replication FailureA Randomized Trial of Bevacizumab for Newly Diagnosed Glioblastoma
Negative / Null Result ReportSpironolactone for Heart Failure with Preserved Ejection Fraction
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1001/jama.2018.8359