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NeoReviews Vol.7 No.9 2006 e474
© 2006 American Academy of Pediatrics

Evidence–based Neonatology

Richard A. Polin, MD^*
John M. Lorenz, MD
David A. Bateman, MD

^* Professor of Pediatrics, Columbia University College of Physicians and Surgeons, New York, NY
Professor of Clinical Pediatrics, Columbia University College of Physicians and Surgeons, New York, NY
Associate Professor of Clinical Pediatrics, Columbia University College of Physicians and Surgeons, New York, NY

The first 300 words of the full text of this article appear below.

	Objectives

 
After completing this article, readers should be able to:

1. Describe strengths and weaknesses of all new sources of information.
   
2. Describe sources of biases and confounding in investigations.
   
3. Explain the potential problems with meta-analyses.
   

	Introduction

 
From the time physicians enter medical school to the time they retire from medicine, they are guided by the principle, "Primum Non Nocere," first do no harm. Although the origins of that phrase are uncertain, it has been widely attributed to Hippocrates who said, "I will prescribe regimens for the good of my patients according to my ability and my judgment and never do harm to anyone." Fundamental to this concept of being a good physician is the acquisition of medical knowledge to provide the highest quality care. Unlike the practice of neonatology 20 years ago, clinicians today are bombarded with incredible amounts of advice and information (scientific meetings, lectures, journals, peers, and the Internet) and ultimately must decide when and if to incorporate a change into clinical practice. In the 1990s, the phrase "evidence-based medicine" became the watchword for the most reliable source of new information. Meta-analyses often are considered the gold standard because an investigator has taken the time to review a topic critically and used a statistical test to confirm or refute the hypothesis. Although meta-analyses are important, they are an imperfect tool and represent only one of several sources of information for the practicing neonatologist. This review critically examines the strengths and weaknesses of new information and provides a primer on commonly used statistical methods in evidence-based medicine. Table 1 lists common definitions for terms used in this article.

Table 1. Definitions

Random sample: Patients in a random sample are representative of the population from which they were drawn. Each data element in the population has an equal chance of being included in the sample. Random sampling is not the same as haphazard sampling. Bias: A systematic difference among/between study groups that in and of itself is related to the exposure/intervention or outcome that results from some aspect of the design or analysis of a research study. Ascertainment bias: Bias introduced by the systematically different determination of an exposure and/or of an outcome in different study groups. Exclusion bias: Bias introduced by the systematic exclusion or loss of eligible subjects from different study groups. Performance bias: Bias introduced by the systematic difference in the management of different study groups other than the intervention of interest. Publication bias: Bias of the published literature that results from a relationship between the likelihood of publication of a research study and whether a relation or effect is demonstrated in the study. Typically, studies that show a negative result, ie, demonstrate no relationship of the outcome to an exposure or no effect of an intervention on the outcome, are less likely to be published. Selection bias: Bias introduced by a systematic difference is the way subjects are assigned to different study groups. Confounding: Systematic differences between/among study groups that in and of itself are related to the exposure/intervention or outcome that occurs naturally, rather than as a result of the design or analysis of the study. Precision: A measure of the variation of the estimate of observation or outcome (be it a prevalence, strength of a relation, or mean values) in study groups, indicated as standard deviation. Validity: A measure of whether the estimated observation or outcome in the study sample approximates the true value. External validity: The degree to which the results of a study are applicable to other samples of the relevant population. Internal validity: The degree to which biases and confounding are avoided in a study.

	Acquiring New Information

 
If there is a challenge for practicing clinicians in the new millennium, it is filtering and assimilating the enormous amount of information received on a daily basis. New therapies are constantly . . . [Full Text of this Article]

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