In previous webinars and articles, we have assessed various aspects of GAMSAT and the topics, themes, and concepts pertaining to the exam. In these articles, we have commenced with an enquiry into the nature and rationale of the topic – we will do the same here with graphs as it is necessary to review this prior to considering graphs in the context of GAMSAT.
What is a graph?
Attempt to answer before moving on. It is important to understand the nature of a graph in order to develop an efficient and critical approach to assessing graphs in GAMSAT. A graph is a visual representation of the relationship between two or more variables (X and Y variables). It is therefore simply a picture of relationships. Before a graph is drawn then, one can consider a graphical relationship in one’s mind and those who regularly think this way will perform to a higher standard in their exam. During next week’s webinar, we will take a close look at common shapes on graphs.
Why use graphs?
Graphs enable more straightforward comprehension of data and make the process of interpretation more efficient. Consider other ways to report data. It may be inefficient and cumbersome to report data in a table, and worse still in written form.
The approach to interpreting graphs
We said that all graphs are visual representations of certain relationships. These relationships exist between variables. Typically, there will be an independent variable (the X-axis, usually time or some intervention) and the dependent variables (the Y-axis). When assessing a graph, take the time to understand the variables and to hypothesise what the relationship may be prior to looking at the body of the graph. In many cases you will be able to hypothesise the relationship based on knowledge from your studies. Students who fail to do this are likely to become confused when looking at the graph.
After predicting what the relationship might be based on your knowledge, you can refer to the body of the graph for the depicted relationship. At this point, you are not interested in forming conclusions, only reporting what you see. The best way to do this is to verbalise your findings in the following manner:
‘As <X-variable> increases, <Y-variable> increases/decreases in a direct/exponential manner.’
In some cases, there may be no change in the dependent variable as the independent variable changes (in which cases there is no relationship).
A common graph seen in GAMSAT pertains to glucose excretion depending on serum concentration (seen below). This is commonly confusing for students.
Looking at the graph, the variables are as follows:
- Independent variable: The amount of sugar in the blood
- Dependent variables:The amount of sugar filtered to form filtrate (not yet urine), the amount of glucose reabsorbed into the blood from the filtrate, and the amount of glucose that is excreted in urine (formally filtrate, and not reabsorbed)
Firstly, consider the relationship between serum glucose and glucose in the filtrate. Without looking at the graph, you will know that this will be a positive and direct relationship. At this point, we are not interested in the other two dependent variables. As serum glucose goes up, filtered glucose will go up. Assessing the graph, we can see this is indeed the case.
Secondly, the relationship between serum glucose and glucose reabsorption from filtrate back into blood can be considered. With knowledge of renal physiology, we would expect that to a certain serum (and therefore filtrate) concentration that all glucose in the filtrate would be reabsorbed, but that at some point the reabsorptive function would become saturated and over a window of concentration that decreasing amount of additional glucose could be absorbed. The relationship would therefore initially be positive and direct before flattening out after which no relationship would exist. This is indeed the case when looking at the purple line in the graph shown.
Finally, consider the relationship between serum glucose and excretion of glucose. As serum glucose increases, we would expect glucose excretion to only increase once reabsorption is saturated. The relationship should therefore commence flat prior to rising up and then becoming positive and direct.
In next week’s webinar, we will look at many more graphs.
Read our last blog post HERE.