# Practice answer keys

One file per practice hospital: the flaws we put in (kind, severity, a plain description, and the evidence a correct find cites), plus reference measures comparing each hospital with hospital_a, the clean reference.

For each measure: `value` is what this hospital shows; `chance_p95` and `chance_p99` are what two clean samples of the same size show by chance 95% and 99% of the time; `verdict` is our call (within chance, borderline, weak, strong). A value above `chance_p99` is unlikely to be chance.

Patients shared across practice hospitals under different ids: hospital_b & hospital_e: 2

## Measures

- `aaa_prev_diff`: AAA prevalence difference (= EMD on the binary label)
- `size_class_hellinger`: Hellinger distance over aaa_size_class
- `diameter_w1_mm`: W1 over max_aortic_diameter_mm, mm
- `age_band_tv`: TV over age band
- `sex_tv`: TV over sex
- `phase_tv`: TV over contrast phase
- `slice_class_tv`: TV over slice_thickness_class
- `vendor_tv`: TV over scanner_vendor
- `feature_c2st_auc`: classifier two-sample AUC, aorta HU and log organ volumes
- `embed_c2st_auc`: classifier two-sample AUC, embedding PCA-32
- `embed_mmd2`: unbiased MMD^2, RBF, embedding PCA-32
- `cmi_s_y_given_x`: I(S;Y|X) in nats, Y = aaa, X = baseline predicted diameter
- `label_pred_w1_mm`: W1 over (label diameter - baseline predicted diameter), mm
- `dice_gap`: |mean baseline aorta Dice difference|
- `aaa_balacc_gap`: |baseline AAA balanced accuracy difference|
- `shortcut_max_phi`: max phi between one study_description or protocol_name value and aaa (values on 3 or more rows)
- `shortcut_u`: I(study_description; aaa) / H(aaa), plug-in (uncertainty coefficient, so prevalence alone does not raise it)
- `neff_deficit_pid`: 1 - n_eff / n, ICC of diameter by released patient_id
- `neff_deficit_emb`: 1 - n_eff / n, ICC of diameter by near-duplicate embedding cluster
- `near_dup_rate`: share of rows with a within-hospital embedding neighbour closer than 0.25