Childhood Adiposity, Adult Adiposity, and Cardiovascular Risk Factors

Structure of your blog entry: You can structure your blog entry however you see fit, hoever you should include the following information:

-Introduction You should briefly describe the purpose and aims of the study you are describing. Remember your audience is GPs and pharmacists.

-Description of methods Think carefully about including the most important details in the methods, because you will not be able to include all the details. You should include details of the experimental protocol, but do not forgot to mention what was measured (and how) and what the primary end point of the study was (or which value was compared between groups.). As your critical analysis is largely dependent on the methods, it is important to make clear how the experiment was conducted.

-Description of results It is important to discuss the most important results quantitatively and to consider the most important information to include in a short summary. Don’t be tempted to write too much about statistical significance, without commenting on the size of the effect measured.

-Critical analysis : Essentially, you should aim to consider the work critically, rather than simply accepting the authors’ conclusion. You can approach this task by asking questions such as: Were the methods (and endpoints) appropriate? What do the results mean? Is the authors’ interpretation of the results supported by the data? You should try to judge each paper on its own merits. You may wish, briefly, to discuss the implications of the research which is again interesting but is not critical analysis. Try to keep your critical analysis specific rather than general for example, rather than automatically saying ‘the experiment would have been better if the sample size had been bigger’ consider whether this is really the case. A comment along the lines of ‘the authors don’t state how they calculated their sample size’ or ‘the authors calculated their sample size but were not able to recruit enough volunteers’ is a much more useful indication that something is wrong. It is important to comment on bias e.g. ‘there were more people with hypertension in the control group than the test group’ and to think carefully about critical analysis of the measurements and endpoints used in the trial. Many studies will claim that drug x reduces cardiovascular risk, when in fact they have only measured the effect of drug x on blood pressure, not on cardiovascular events. If a trial uses the ‘Penson depression score’ as its endpoint, you need to question what this score is? What does it mean? Has it been validated in other trials? What are its strengths and weaknesses?. Some experiments don’t seem to have a clear hypothesis and don’t state the primary endpoint in the methods. This is often the case with trials involving mental health where the patients will be assessed for severity of symptoms using 4 or 5 different scales before and after an intervention. You should be asking if it is necessary to use so many scales , or whether the authors were ‘hedging their bets’ and hoping they would see a significant difference in at least one of the measurements. It is also interesting to comment on the way in which numerical data are treated. For example, Some trials of antihypertensives set out arbitrary categories for BP (ie normal<140/90<hypertensive) and then presented their results saying ‘at the end of the trial 20% of people in the control group were hypertensive and 10% of people in the treatment group were hypertensive’ When data is categorised like this, it is a good idea to ask why? Is there a good reason? Or would it have been better to present the mean BP in each group? You may find it helpful to commented on the statistics used in published work. Often these are very ropy!

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