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id
string
case_context
string
vessel_tortuosity
float64
calcification_burden
float64
lesion_length_mm
int64
segmentation_confidence
float64
image_artifact_score
float64
ai_ffr_prediction
float64
ai_ffr_run_variance
float64
model_disagreement
float64
invasive_ffr_ground_truth
float64
abs_error
float64
discordance_flag
int64
discordance_type
string
subgroup_risk_label
string
reliability_drop_score
float64
notes
string
constraints
string
gold_checklist
string
APDD-001
clean vessel
0.2
0.1
12
0.95
0.06
0.84
0.01
0.02
0.83
0.01
0
none
none
0.08
aligned baseline
<=280 words
flag+type+subgroup+risk
APDD-002
moderate calcification stable
0.38
0.45
20
0.86
0.1
0.78
0.02
0.05
0.75
0.03
0
calcification-tolerant
moderate-calcified
0.22
still within band
<=280 words
flag+type+subgroup+risk
APDD-003
heavy calcification silent drift
0.4
0.75
26
0.78
0.14
0.76
0.03
0.06
0.67
0.09
1
calcification-driven overestimate
heavily-calcified
0.62
AI looks plausible but wrong
<=280 words
flag+type+subgroup+risk
APDD-004
high tortuosity segmentation wobble
0.7
0.2
22
0.72
0.12
0.74
0.05
0.1
0.66
0.08
1
tortuosity-driven instability
high-tortuosity
0.58
variance spikes
<=280 words
flag+type+subgroup+risk
APDD-005
mixed anatomy + artifacts
0.55
0.65
28
0.68
0.26
0.75
0.06
0.14
0.63
0.12
1
artifact-amplified discordance
calcified+artifact
0.74
artifact magnifies error
<=280 words
flag+type+subgroup+risk
APDD-006
long lesion low confidence
0.5
0.4
40
0.62
0.18
0.73
0.06
0.12
0.64
0.09
1
low-confidence under-call risk
long-lesion+low-conf
0.66
confidence collapse
<=280 words
flag+type+subgroup+risk

Goal

Detect discordance
between coronary anatomy complexity
and AI-derived FFR accuracy
against invasive FFR ground truth.

This targets silent degradation
in specific patient subgroups.

Inputs

  • vessel_tortuosity
  • calcification_burden
  • lesion_length_mm
  • segmentation_confidence
  • image_artifact_score
  • ai_ffr_prediction
  • ai_ffr_run_variance
  • model_disagreement
  • invasive_ffr_ground_truth

Required outputs

  • discordance_flag
  • discordance_type
  • subgroup_risk_label
  • reliability_drop_score

Discordance types

Examples:

  • calcification-driven overestimate
  • tortuosity-driven instability
  • artifact-amplified discordance
  • low-confidence under-call risk

Why it matters

AI can look stable
while failing on:

  • heavily calcified vessels
  • high tortuosity anatomy
  • long lesions with low confidence
  • artifact-heavy scans

Validators need to know
where accuracy drops
before it shows up
as a headline metric failure.

Evaluation

The scorer checks that the response includes:

  • a binary discordance flag
  • a named discordance type
  • a subgroup label
  • a 0 to 1 reliability drop score
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