<｜begin▁of▁sentence｜>### Instruction:
Imagine you're charting the progress of a patient's journey through various stages of therapy, and you've mapped their journey in a step-by-step process. You've noted their progress as a series of transitions from one stage to the next — starting at stage 1, moving to stage 2, then 3, and so on until they reach stage 5. This progress is currently represented as consecutive connections: ((1 to 2), (2 to 3), (3 to 4), (4 to 5)). 

In your nursing assessments, it could be insightful to visualize not only the direct transitions but also to acknowledge potential overall progressions that your patient could make. In other words, you might want to determine all the hypothetical, direct, or indirect steps your patient could take from their initial to final stages in their therapy journey. 

Using the digital tool NetworkX, how could you transform this stepwise progression into a comprehensive overview, essentially calculating the full spectrum of therapeutic pathway combinations implied by the connected steps you've observed? Keep in mind, this inquiry is solely about understanding the broader structure of the patient's therapeutic course.
### Response:
import networkx as nx

# Create a directed acyclic graph (DAG)
G = nx.DiGraph()
G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 5)])

# Compute the transitive closure of the DAG
TC = nx.transitive_closure_dag(G)
print('Transitive closure edges:', TC.edges())
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