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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