How to Make Meaningful Conclusions here?
It is very difficult (arguably impossible, if you want to get philosophical about it) to be absolutely, 100%, for sure about anything. For this reason, we talk in terms of probability/significance/confidence sets. A refresher on statistical hypothesis testing might help.
So to answer this type of question, people would usually try to attain a well-agreed-upon p-value for their problem, below which we can reject the null hypothesis, and above which we accept the null hypothesis. The null hypothesis in this case is that the bone disease is not a symptom of COVID, and the alternative would be that it is a symptom.

Edit for demonstration as requested in the comments:
Firstly, these methods are purely for association analysis, not for proving whether bone disease is a symptom of COVID—again, correlation $\ne$ causation!
Given that we are dealing with binary variables, you could use the Phi coefficient to measure the association of bone disease with COVID.
consider this contingency matrix:
|----------|---------|---------|-----------|
|          | Bone =0 | Bone =1 | total     |
| COVID =0 |    A    |  B      | I=A+B     |
| COVID =1 |    C    |  D      | J=C+D     |
|------------------------------|-----------|
|  total   |  K=A+C  |  L=B+D  | E=I+J+K+L |
|----------|---------|---------|-----------|

Which we can represent visually via a Venn diagram:

then you can calculate
$ \phi=\frac{AD-BC}{\sqrt{IJKL}}=\frac{ED-IK}{\sqrt{IK(E-I)(E-K)}} $
This is related to the Chi-squared test: $ \phi= \sqrt{\frac{\chi^2}{n}} $ So you can easily retrieve the p-value given that you know the degrees of freedom (in this case, it is just 1).
And you interpret it similar to the Pearson correlation coefficient (both from the same statistician—Pearson).

So given $7.5\%$ of the total has the bone disease, and $25\%$ of COVID patients have it, we can construct our contingency table in terms of $N$ (the number of samples). Where $Q$ is the percent of people with COVID:
$ I=N(1-Q)= $ number of people without COVID
$ J=NQ= $ number of people with COVID
$ K=N(1-0.075)=$ number of people without bone disease
$ L=N(0.075)=$ number of people with bone disease
We know that 25% of people with COVID also have the bone disease, so $D=J(0.25)\Rightarrow$
$\phi=\frac{E(0.25\cdot J)-IK}{\sqrt{IK(E-I)(E-K)}}$
Finally, we can calculate:
$\phi=\frac{0.25\cdot EQ-N(1-Q)(1-0.075)}{\sqrt{(1-Q)(1-0.075)(E-N(1-Q))(E-N(1-0.075))}}$
From here, we can find the associated p-value easily by looking it up in a Chi-Square p-value table, such as this one: http://chisquaretable.net/. Then you can accept/reject the null hypothesis given your predefined $\alpha$ threshold.