What is the meaning of percentage in parantheses and the p-value in this bivariate analysis of patients? In this paper, Table 2 shows some values in parentheses. The paper doesn't seem to include an explanation.
As an example, 82 patients out of 94 with Radial head fracture showed No HO (Heterogeneous Ossification) whereas 12 patients showed HO. But what do the 25% and 31% in the parentheses mean?
Additionally, I will be grateful if the experts here could also explain the meaning of the p-value in this context? [I know the meaning of the p-value in general.]

 A: Some of the percentages seem to refer to the $n$ per column, for instance, there are $9$ Females among the $n=27$ participants with HO restricting motion, $9/27=33\%$. Same for Males. However, this does not work for most of the other percentages, e.g., the one you highlighted.
For the $p$ values, it would make sense for them to come out of contingency table tests, and the paper notes they used Fisher's exact test. For the row you highlighted, this works. In R:
> fisher.test(cbind(c(82,257-82),c(12,27-12)))

        Fisher's Exact Test for Count Data

data:  cbind(c(82, 257 - 82), c(12, 27 - 12))
p-value = 0.2015
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.2438647 1.4403518
sample estimates:
odds ratio 
 0.5868749

Note we get $p=0.2$, as in the table. However, for the next row, it doesn't:
> fisher.test(cbind(c(112,257-112),c(7,27-7)))

        Fisher's Exact Test for Count Data

data:  cbind(c(112, 257 - 112), c(7, 27 - 7))
p-value = 0.1005
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.8563294 6.3857329
sample estimates:
odds ratio 
  2.201139 

R gives us $p=0.1$, while the table says $p=0.07$.
I suspect simple errors and sloppiness - probably these numbers came out of a spreadsheet that was not cleanly updated. You could contact the authors and ask them. However, you may not be lucky when you point out what may very well be errors on their part and ask them to recalculate their numbers. They may not be enthusiastic about that.
