To compare the cause and manner of death between the clinical impression and the autopsy findings, you will likely use statistical tests that assess the agreement between two categorical variables. Here are a few options:
Cohen's Kappa: This test measures the agreement between two raters (in this case, clinical impression and autopsy findings) for categorical items. It is a robust method for determining the level of agreement beyond what would be expected by chance.
Chi-Square Test for Independence: This test evaluates whether there is a significant association between two categorical variables. It is useful for examining if the distribution of causes and manners of death differs between the clinical impression and autopsy findings.
McNemar's Test: If you have paired nominal data (e.g., each patient has a clinical impression and an autopsy finding), McNemar's test is appropriate for determining if there are differences in the paired proportions.
Fleiss' Kappa: If you have more than two raters or multiple methods of determining the cause and manner of death, Fleiss' Kappa can be used to measure the agreement between them.
That’s a great question. Technology has evolved drastically. You could use a statistical approach such as creating a tool
integrating advanced statistical methods with forensic-specific algorithms, making the comparison process much more efficient and accurate. For example, you could use a McNemar's test to compare categorical data like cause and manner of death between the clinical and autopsy findings. If there were any continuous data involved, it could also use a paired t-test or Wilcoxon signed-rank test, depending on the data distribution.
It’s a bit of a futuristic idea, but definitely something that could transform how we approach these comparisons.