<｜begin▁of▁sentence｜>### Instruction:
As an Emergency Medical Technician (EMT), you're tasked with providing urgent medical care and transportation to patients in critical situations. Imagine you're dispatched to a scene where a network of interconnected roads represents a patient's circulatory system. Your task is to navigate this network efficiently, prioritizing the most critical intersections for assessment and treatment.

Now, consider this scenario: You're handed a map indicating the roads (edges) connecting different intersections (nodes). These intersections represent the flow of blood in the patient's body. Your goal is to perform a maximum cardinality search, essentially identifying the most vital intersections based on their connectivity and potential impact on the patient's condition.

Utilizing tools like NetworkX, you can analyze this network, determining the optimal order in which to assess these intersections, much like prioritizing critical patients in an emergency room. Additionally, you'll need to assess if the network exhibits chordal characteristics, akin to identifying structural weaknesses or potential complications in the patient's circulatory system.

Your expertise in efficiently navigating both physical and abstract networks can make a significant difference in ensuring timely and effective medical care for the patient.
### Response:
import networkx as nx

# Create a new undirected graph
G = nx.Graph([(0, 1), (1, 2), (2, 3), (3, 4), (4, 0), (1, 3)])

# Perform maximum cardinality search on the graph
search_result = nx.maximum_cardinality_search(G)

# The search_result is a dictionary where keys are the node and values are the position of that node in search ordering
search_ordering = {node: rank for rank, node in enumerate(sorted(search_result, key=search_result.get, reverse=True))}

# Check if the graph is chordal
is_chordal_graph = nx.is_chordal(G)

# Output the search ordering and if the graph is chordal
print('Search Ordering:', search_ordering)
print('Is the graph chordal?:', is_chordal_graph)
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