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The /mappings endpoint returns all loaded concept mappings between NAMASTE codes and ICD-11 codes. Each mapping carries a confidence score indicating the strength of the equivalence relationship between the source and target concept.

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Endpoint

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GET /mappings
string
Method: GET
Path: /mappings
Auth: None (demo mode)
Query Parameters: None

​
Response

Returns a JSON array of mapping objects. Each object contains the following fields:
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sourceCode
string
The NAMASTE code being mapped (e.g. NAM-001).
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targetCode
string
The ICD-11 code this NAMASTE code maps to (e.g. TM2-001 or BM-001).
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equivalence
string
The type of equivalence between source and target. One of equivalent, equal, wider, or subsumes.
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sourceSystem
string
The coding system of the source code. Always "NAMASTE".
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targetSystem
string
The coding system of the target code. Either "ICD-11 TM2" or "ICD-11 Biomedicine".
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confidence
number
A float between 0.0 and 1.0 representing the accuracy of the mapping.

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Example

curl -X GET http://localhost:5000/mappings
Response
[
  {
    "sourceCode": "NAM-001",
    "targetCode": "TM2-001",
    "equivalence": "equivalent",
    "sourceSystem": "NAMASTE",
    "targetSystem": "ICD-11 TM2",
    "confidence": 0.9
  },
  {
    "sourceCode": "NAM-002",
    "targetCode": "TM2-002",
    "equivalence": "equivalent",
    "sourceSystem": "NAMASTE",
    "targetSystem": "ICD-11 TM2",
    "confidence": 0.85
  },
  {
    "sourceCode": "NAM-011",
    "targetCode": "BM-001",
    "equivalence": "equivalent",
    "sourceSystem": "NAMASTE",
    "targetSystem": "ICD-11 Biomedicine",
    "confidence": 0.87
  }
]

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Equivalence Types

The equivalence field describes the semantic relationship between the source concept and its ICD-11 target.
ValueMeaning
equivalentThe source and target represent the same clinical concept.
equalThe source and target are an exact textual and semantic match.
widerThe source concept is broader than the target concept.
subsumesThe source concept contains (subsumes) the target concept.

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Confidence Score

The confidence field is a float ranging from 0.0 (no confidence) to 1.0 (perfect confidence), indicating how accurately the source code maps to the target. The 85 mappings in the sample dataset collectively maintain approximately 85% coverage of the loaded NAMASTE code set.
Mappings are persisted to the database at startup. If the database is empty when the application starts, mappings are loaded from the in-memory fallback dataset defined in app.py.

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