By L. Santiago Medina, Kimberly E. Applegate, C. Craig Blackmore
This functional booklet is perfect for readers who are looking to swiftly ascertain the best imaging for pediatric sufferers. The textual content presents a concise and available precis of the literature on how and while to exploit imaging reports. Chapters tackle the necessities, comparable to cost-effectiveness, and are written in collaboration by way of popular experts within the fields of pediatrics and pediatric radiology. subject matters hide universal scientific situations in neuroimaging and musculoskeletal, chest, and stomach imaging. each one imaging advice is gifted in addition to the helping facts and the energy of the evidence.
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Additional resources for Evidence-Based Imaging in Pediatrics: Improving the Quality of Imaging in Patient Care
This random allocation allows for unbiased distribution of both known and unknown confounding variables between the study groups. In nonrandomized studies, appropriate study design and statistical analysis can control only for known or measurable bias. Detection of and correction for bias, or systematic error, in research is a vexing challenge for both researchers and users of the medical literature alike. Maclure and Schneeweiss (11) have identified 10 different levels at which biases can distort the relationship between published study results and truth.
98, respectively. 92, respectively. , sensitivity and specificity) does not depend on the prevalence (pretest probability) of the disease. CTA, CT angiogram. References 1. Levin A. Ann Intern Med 1998;128:334–336. 2. Evidence-Based Medicine Working Group. JAMA 1992;268:2420–2425. 3. The Evidence-Based Radiology Working Group. Radiology 2001;220:566–575. 4. Wood BP. Radiology 1999;213:635–637. 5. Poisal JA et al. Health Affairs 2007 (Feb 21): W242–253. 6. Davis K. NEJM 2008;359(Oct 17):1751–1755.
Cost-utility analysis is similar to CEA except that the effectiveness also accounts for quality of life issues. Quality of life is measured as utilities that are based on patient preferences (16). The most commonly used utility measurement is the quality-adjusted life year (QALY). The rationale behind this concept is that the QALY of excellent health is more desirable than the same 1 year with substantial morbidity. The QALY model uses preferences with weight for each health state on a scale from 0 to 1, where 0 is death and 1 is perfect health.