Showing posts with label Sensitivity and Specificity. Show all posts
Showing posts with label Sensitivity and Specificity. Show all posts
Friday, March 9, 2012
ROC Curves
Click to watch a "5 Minutes of EBM" session on ROC curves and how to interpret them, delivered in association with Dr. Villanueva's presentation at our last Journal Club.
Thursday, March 1, 2012
Sensitivity and Salmon
This week we talked briefly about sensitivity and predictive value, and reviewed a fantastic poster by Bennett et al. Here is methods section, in toto:

Let's just translate this briefly. What Bennett is saying is that he bought a dead salmon to calibrate his fMRI machine, and that, like a good scientist, he performed the entire experiment he intended to run on humans on the salmon in order to control for all potential unmeasured variables.
Reminding us once again of the striking graphic efficacy of fMRI results, here's a slice from his scan:

As you can see, the salmon appears to be...ah...."mentalizing."
Bennett is to be commended in publishing this cautionary tale, whcih reminds us that high sensitivity is not always a good thing. In addition, this is about the best illustration I can think of to explain why predictive value depends on prevalence. If you're testing whether a dead fish has the capacity to perform a "mentalizing task," then all results are false positive results, because, unless fMRI counts among its widely trumpeted virtues the power of resurrection, dead salmon don't mentalize.
Let's just translate this briefly. What Bennett is saying is that he bought a dead salmon to calibrate his fMRI machine, and that, like a good scientist, he performed the entire experiment he intended to run on humans on the salmon in order to control for all potential unmeasured variables.
Reminding us once again of the striking graphic efficacy of fMRI results, here's a slice from his scan:
As you can see, the salmon appears to be...ah...."mentalizing."
Bennett is to be commended in publishing this cautionary tale, whcih reminds us that high sensitivity is not always a good thing. In addition, this is about the best illustration I can think of to explain why predictive value depends on prevalence. If you're testing whether a dead fish has the capacity to perform a "mentalizing task," then all results are false positive results, because, unless fMRI counts among its widely trumpeted virtues the power of resurrection, dead salmon don't mentalize.
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