Without reading the paper, the following is my interpretation.

1. Each concussion was analyzed as an independent event.

- They did not control for players contributing more than 1 concussive episode in the study. Example, if I was trying to find the average weight of everyone that came to my restaurant, I would find the average, though some patrons may have came more than once and would contribute more information. When modeling, you typically want to examine to see if controlling for multiple observations helps to better explain the outcome or not. So as per my bad example, if the person had unique traits or trends (say they were 330 pounds), knowing they are in the dataset 4 times can help explain the outcome better.

2. Summary measures are reported with 95% confidence intervals and significant differences with P<0.05.

They have a single sample, which may vary from the population at large, so it is typical to provide 95% CI to describe where the true measure may be.

Differences: they are saying they compared values and used a statistical test and if the p-value for the test was below 0.05 is was determined to be statistically significant beyond their acceptable level of chance.

3. Injury rates per 100 game positions (gp) were calculated to provide perspective on the risk of injury among the position categories." (Casson et al. 2010)

# concussion per 100 games, and they may have broken this out by NFL position. So over a hundred games how many centers sustained a concussion. So if there were 330 concussions in centers over 10000 game the risk of concussion for a center per 100 games would be 3.3%.

I wrote this quickly, so let me know if you need clarification.