Interpreting the Medical Literature: Fifth Edition

by Stephen H. Gehlbach

Book Overview

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Cut Through the Complexities of Medical Studies

A Doody's Core Title ESSENTIAL PURCHASE

Enhance your understanding and utilization of the information in medical journals with Stephen Gehlbach's Interpreting...

• Format: Paperback
• Language: English
• ISBN: 0071437894
• ISBN13: 9780071437899
• Release Date: March 2006
• Publisher: McGraw Hill / Medical
• Length: 308 Pages
• Weight: 0.98 lbs.
• Dimensions: 0.8" x 6.1" x 9.0"

Customer Reviews

Great book for medical epidemiology!

I bought this book because I had no training in statistics and was feeling frustrated by having to memorize meaningless things for exams. I mean, that the short term memorization of sensitivity/specificity, prevalence, odds rations, these kinds of things was hard for me and then I would forget what they meant immediately after the exam. That is not the best way to learn medical stats. So, in despair I looked for a good book to actually learn what is behind the short-hand notation I had been learning. This book actually teaches what it all means! The author is also pretty friendly and funny and I found that his humor was really helpful for absorbing the points he was making. He took the basic information and expanded it, so that now I really do understand the different studies and how to interpret their results. Well, I am no expert but at least I have a decent working knowledge of what is behind a research article. Good book, and it explains things very well.

Good Reference

A statistician might find Gehlbach's explanations a bit too simplistic, but I think it gives a fantastic overview of how to approach interpretation of journal articles. An attending asked us to buy the second edition of this book when I was a 3rd Year med student, and I still use it today teaching residents. He does an excellent job using examples from the literature to illustrate his points. And, unlike other statistical texts, the index can help you grasp topics like the null hypothesis or type I error very quickly.