{
  "context": "Evaluates whether the solution relies on fhirpy's SearchSet query builder to compose and run Observation/Patient queries for the specified tasks. Checks correct use of search filters, limits, sorting, element selection, includes, and reverse chaining instead of manual HTTP calls or client-side filtering.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Search filters",
      "description": "Uses SearchSet.search (or SQ) parameters to apply patient reference, code list, and since filters rather than constructing URLs manually or filtering results after fetch.",
      "max_score": 20
    },
    {
      "name": "Limit & sort",
      "description": "Applies SearchSet.limit(max_results) and SearchSet.sort('-effectiveDateTime' or equivalent recency field) so returned Observations/Patients respect size caps and newest-first ordering.",
      "max_score": 20
    },
    {
      "name": "Element selection",
      "description": "Uses SearchSet.elements(...) (with exclude flag when needed) to restrict Observation fields for summary_only responses instead of trimming data manually.",
      "max_score": 15
    },
    {
      "name": "Includes",
      "description": "Adds include clauses via SearchSet.include (and revinclude if used) to bundle related Patient and Practitioner resources with Observation queries, avoiding separate fetches or manual joins.",
      "max_score": 15
    },
    {
      "name": "Reverse chaining",
      "description": "Finds Patients with matching Observations through SearchSet.has(...) or explicit _has parameters (via SQ), not by fetching Observations then filtering/iterating in Python.",
      "max_score": 20
    },
    {
      "name": "Search execution",
      "description": "Executes queries with SearchSet fetch helpers (fetch, fetch_all, or fetch_raw) and uses include_observations paths to return bundles rather than hand-built HTTP requests.",
      "max_score": 10
    }
  ]
}