Based on my prior knowledge, I can analyze the relationship between the feature "fibr_ter_06" (Fibrinolytic therapy by Celiasum 500k IU) and the task of determining whether the patient shows chronic heart failure.

To carry out this analysis, I would need access to a dataset containing the feature values and the corresponding target variable (whether the patient has chronic heart failure or not).

Here's how I would approach the analysis:

1. Load the dataset: Load the dataset that includes the feature "fibr_ter_06" and the target variable indicating the presence of chronic heart failure.

2. Explore the data: Examine the distribution of the feature values and the target variable across the dataset. Calculate the frequency and proportion of each category of "fibr_ter_06" (e.g., "no" and "yes") within the target variable classes (patients with and without chronic heart failure).

3. Analyze the relationship: Compare the distribution of "fibr_ter_06" values between the target variable classes. Calculate the conditional probabilities and chi-square test statistics to determine whether there is a significant association between "fibr_ter_06" and the presence of chronic heart failure.

4. Create the dictionary: Based on the analysis, create a dictionary with the possible values of "fibr_ter_06" for each target class ("no" and "yes"). Include only the values that are significantly associated with the presence or absence of chronic heart failure.

Here's an example of how the dictionary might look like:

```json
{
	"no": ["no", "yes"],
	"yes": ["yes"]
}
```

In this example, the feature value "yes" of "fibr_ter_06" is significantly associated with chronic heart failure, while both "no" and "yes" values are associated with the absence of chronic heart failure.