Calculate and validate SCORE2-Diabetes test expected values

[fbraza]

Issue Description

The SCORE2-Diabetes implementation (PR #16) includes test structure with placeholder values that need to be calculated and validated using MDCalc before the tests can be properly executed.

Problem

• All test expected values in tests/test_score2_diabetes.py are placeholders (0.00, "PLACEHOLDER")
• Test assertions are commented out, preventing actual validation
• 6 test cases covering diverse scenarios need proper expected values calculated

Test Cases Requiring Validation

Test Files in tests/inputs/score2_diabetes/:

1. Patient 01: Male, 45 years, non-smoker, good diabetes control (HbA1c: 48)
2. Patient 02: Female, 55 years, smoker, poor control (HbA1c: 58)
3. Patient 03: Male, 65 years, non-smoker, very poor control (HbA1c: 68, low eGFR: 60)
4. Patient 04: Female, 48 years, non-smoker, excellent control (HbA1c: 42)
5. Patient 05: Male, 60 years, smoker, poor control (HbA1c: 75, very low eGFR: 50)
6. Patient 06: Female, 42 years, smoker, moderate control (HbA1c: 53)

Required Actions

Phase 1: Calculate Expected Values

• Use MDCalc SCORE2-Diabetes calculator for each test case
• Record calculated risk percentages
• Determine risk categories based on age groups:
  • <50 years: Low to moderate (<2.5%), High (2.5-7.5%), Very high (≥7.5%)
  • 50-69 years: Low to moderate (<5%), High (5-10%), Very high (≥10%)

Phase 2: Update Test File

• Replace placeholder values with calculated values
• Uncomment test assertions:

  assert pred_risk_category == expected_category
  assert pytest.approx(pred_risk, abs=0.1) == expected_risk

• Update test parametrize decorator with real expected values

Phase 3: Comprehensive Edge Case Testing

• Add edge case tests for boundary values
• Test age boundaries (40, 49, 50, 69 years)
• Test extreme biomarker values within reasonable ranges
• Test different diabetes control scenarios

Example Expected Format

@pytest.mark.parametrize(
    "filename,expected",
    [
        ("test__input__patient_01.json", (45.00, 8.52, "High")),  # Example values
        ("test__input__patient_02.json", (55.00, 12.34, "Very high")),
        # ... etc
    ],
)

Success Criteria

• All 6 test cases have validated expected values
• Tests pass when run with make test
• Risk categories align with age-specific thresholds
• Test coverage includes diverse diabetes scenarios

Priority

High - This blocks proper validation of the SCORE2-Diabetes implementation

Related

• PR feat: implement SCORE2-Diabetes cardiovascular risk algorithm #16: feat: implement SCORE2-Diabetes cardiovascular risk algorithm
• SCORE2-Diabetes specification in specs/score2_diabetes.md
• Existing SCORE2 test patterns in tests/test_score2.py

Notes

• Use Belgium (Low Risk) region calibration as implemented
• Ensure calculated values match the implementation's Belgium calibration
• Consider adding comments explaining specific risk scenarios for each test case