Topic: Blog1

Topic: Blog1

Order Description

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. You shouldn’t need to give long

explanations of common medical conditions. Remember that the marks are awarded for your description and critical analysis of the research.

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

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. Many papers will include lots of measurements, you need to

consider which are the most important, as you won’t have room to discuss them all.

Critical analysis

This is probably the most difficult section (and consequently, where the most marks are available). 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. If a paper set out to test the hypothesis that ‘dogs enjoy eating bones’ it’s not really fair to criticise it for

not asking whether cats like eating bones. Proposing an extension to a study (however interesting) is not critical analysis. 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. In very may experiments it is true, but it requires some justification. Experimenters don’t usually pick a

sample size (n number) out of thin air, they will perform calculations to work out the sample size they need. A trial that is bigger than it needs to be costs more

money and may have ethical implications relating to unnecessary experimentation on volunteers or animals. 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!

These are by no means the only things you can comment on, but hopefully they give some food for thought.

In order to write a blog entry about a piece of research, you should know the paper ‘inside-out’ and understand it. My strong advice would be to try to write the blog

entry without the paper in front of you. You will obviously need to check figures and details, but this approach should help you to avoid any allegations of academic

misconduct, for which the sanctions are very serious. University advice regarding academic misconduct and plagarism can be found here:

http://www.ljmu.ac.uk/studysupport/81924.htm

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