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Add hospital tables, answer keys, crosswalk and dataset card

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README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ pretty_name: SlicePoint Hospitals
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+ language: [en]
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+ tags: [medical, ct, abdominal-aortic-aneurysm, federated-learning, non-iid, distribution-shift, dataset-shift, nifti]
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+ size_categories: [n<1K]
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+ configs:
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+ - config_name: features
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+ data_files:
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+ - {split: hospital_a, path: hospital_a/features.parquet}
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+ - {split: hospital_b, path: hospital_b/features.parquet}
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+ - {split: hospital_c, path: hospital_c/features.parquet}
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+ - {split: hospital_d, path: hospital_d/features.parquet}
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+ - {split: hospital_e, path: hospital_e/features.parquet}
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+ - {split: hospital_f, path: hospital_f/features.parquet}
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+ - {split: hospital_g, path: hospital_g/features.parquet}
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+ - config_name: labels
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+ data_files:
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+ - {split: hospital_a, path: hospital_a/labels.parquet}
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+ - {split: hospital_b, path: hospital_b/labels.parquet}
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+ - {split: hospital_c, path: hospital_c/labels.parquet}
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+ - {split: hospital_d, path: hospital_d/labels.parquet}
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+ - {split: hospital_e, path: hospital_e/labels.parquet}
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+ - {split: hospital_f, path: hospital_f/labels.parquet}
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+ - {split: hospital_g, path: hospital_g/labels.parquet}
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+ - config_name: predictions
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+ data_files:
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+ - {split: hospital_a, path: hospital_a/predictions.parquet}
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+ - {split: hospital_b, path: hospital_b/predictions.parquet}
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+ - {split: hospital_c, path: hospital_c/predictions.parquet}
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+ - {split: hospital_d, path: hospital_d/predictions.parquet}
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+ - {split: hospital_e, path: hospital_e/predictions.parquet}
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+ - {split: hospital_f, path: hospital_f/predictions.parquet}
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+ - {split: hospital_g, path: hospital_g/predictions.parquet}
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+ - config_name: embeddings
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+ data_files:
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+ - {split: hospital_a, path: hospital_a/embeddings.parquet}
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+ - {split: hospital_b, path: hospital_b/embeddings.parquet}
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+ - {split: hospital_c, path: hospital_c/embeddings.parquet}
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+ - {split: hospital_d, path: hospital_d/embeddings.parquet}
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+ - {split: hospital_e, path: hospital_e/embeddings.parquet}
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+ - {split: hospital_f, path: hospital_f/embeddings.parquet}
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+ - {split: hospital_g, path: hospital_g/embeddings.parquet}
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+ - config_name: crosswalk
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+ data_files: crosswalk.parquet
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+ ---
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+
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+ # SlicePoint Hospitals
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+
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+ Seven simulated hospitals for studying dataset shift and non-IID federated learning on
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+ abdominal CT, with the answer keys. Each hospital is a sample of the
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+ [SlicePoint AAA CT](https://huggingface.co/datasets/slicepoint/slicepoint-aaa-ct) scans,
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+ and all but two have a problem planted on purpose: the kind of difference that makes an
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+ AI model behave differently at a new site. They were built for the 2026 Nucleate
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+ Pittsburgh hackathon, where teams had to find the problems. The game is over, so the keys
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+ are published.
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+
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+ **Not for clinical use.** AAA labels come from a diameter rule on segmentation masks.
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+
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+ ## The hospitals
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+
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+ | Hospital | Scans (patients) | AAA | What is different | How clearly it shows |
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+ |---|---|---|---|---|
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+ | `hospital_a` | 100 (100) | 9 | Nothing. A clean random sample; the reference the others are compared with | none |
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+ | `hospital_b` | 80 (40 people under 60 IDs) | 7 | Patients scanned again and again, some under new IDs; 2 patients reappear at `hospital_e` | strong (repeats), weak (cross-site) |
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+ | `hospital_c` | 80 (80) | 7 | Every scan thick-slice (5 mm) and venous phase: same patients, scanned differently | strong on acquisition, weak on Dice |
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+ | `hospital_d` | 80 (80) | 20 | Far more aneurysms, and older, more male, more arterial phase, all moving together | strong; the factors are confounded |
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+ | `hospital_e` | 80 (80) | 7 by its own rule | Labels measure the inside of the vessel and call AAA at 25 mm: same images, different truth | strong |
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+ | `hospital_f` | 80 (80) | 7 | Nothing. Tests for false alarms | none |
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+ | `hospital_g` | 80 (80) | 7 | The exam name gives away the answer (a shortcut), plus 2 `hospital_b` patients under new IDs | weak |
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+
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+ `keys/<hospital>.json` holds the full recipe of each hospital: the planted findings
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+ (`waldos`: kind, severity, description, the evidence a correct finding cites), the
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+ targets, what was realised, and one row per case (source scan, repeat or cross-site copy,
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+ new patient id or not). `keys/practice/` has the reference measures given to teams for
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+ `hospital_a` to `hospital_e`, with the chance levels two clean samples would show.
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+
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+ `crosswalk.parquet` maps every case to its scan in SlicePoint AAA CT
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+ (`canonical_image_id`), its true patient, and whether its image was changed:
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+ `hospital_c` ships 5 mm re-makes, and the repeat scans at `hospital_b` and `hospital_g`
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+ have added noise and a few slices dropped, so no file is a byte copy of another.
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+
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+ ## Layout
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+
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+ | Path | What |
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+ |---|---|
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+ | `hospital_<x>/images/<case_id>.nii.gz` | CT volume (1.5 mm; `hospital_c` 1.5 x 1.5 x 5 mm) |
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+ | `hospital_<x>/masks/<case_id>_aorta.nii.gz` | Ground-truth aorta mask on the same grid |
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+ | `hospital_<x>/{features,labels,predictions,embeddings}.parquet` | The SlicePoint input contract, joined on `case_id` |
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+ | `keys/`, `crosswalk.parquet` | Answer keys, as above |
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+
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+ Column meanings are the same as in SlicePoint AAA CT. `study_description` and
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+ `protocol_name` are synthetic; at `hospital_g` they carry the shortcut.
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+
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+ ## Download and use
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+
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+ ```bash
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+ hf download slicepoint/slicepoint-hospitals --repo-type dataset --exclude "*/images/*" "*/masks/*" # tables + keys, ~10 MB
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+ hf download slicepoint/slicepoint-hospitals --repo-type dataset --include "hospital_c/*" # one site with images
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+ ```
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+
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+ ```bash
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+ slicepoint assess --data hospital_b --site-id hospital_b \
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+ --reference-dir hospital_a --reference-site-id hospital_a --html report.html
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+ ```
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+
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+ For federated-learning experiments, treat each `hospital_<x>` folder as one client;
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+ `crosswalk.parquet` tells you where clients share patients.
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+
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+ ## Source, license and citation
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+
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+ Derived from the **TotalSegmentator dataset v2.0.1** (Wasserthal J. et al., University
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+ Hospital Basel, [doi:10.5281/zenodo.10047292](https://doi.org/10.5281/zenodo.10047292);
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+ acquisition details from v3.0.0,
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+ [doi:10.5281/zenodo.22688904](https://doi.org/10.5281/zenodo.22688904)), licensed
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+ [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Modified by Asher Informatics
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+ PBC (sampled, perturbed as described above, re-identified, derived tables added) and
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+ released under CC BY 4.0. Segmentations by the TotalSegmentator `total` model
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+ (Apache-2.0). Embeddings by [Merlin](https://huggingface.co/stanfordmimi/Merlin) (MIT).
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+ Please cite TotalSegmentator (Radiology: AI 2023,
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+ [doi:10.1148/ryai.230024](https://doi.org/10.1148/ryai.230024)).
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keys/hospital_b.json ADDED
@@ -0,0 +1,2069 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "display_name": "Surveillance Clinic",
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+ "strength": "none",
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+ "vocab_dir": "recipe",
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+ "perturb": "table stand-in until rerun",
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+ "realised": {
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+ }
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+ "patient_id": "rehash_partial"
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+ },
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+ "waldos": [
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+ {
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+ "kind": "clustering",
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+ "severity": "strong",
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+ "description": "Many patients scanned repeatedly, some under fresh IDs. Looks like 80 cases; holds far fewer independent ones.",
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+ "evidence_expected": "Near-duplicate embeddings within the hospital, ICC and design effect, n_eff well below n, naive CIs too narrow."
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+ },
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+ {
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+ "kind": "clustering",
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+ "severity": "weak",
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+ "description": "A handful of patients also appear at the Reader Drift Hospital under new IDs.",
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+ "evidence_expected": "Cross-hospital near-duplicate pairs; a hospital-level split would leak."
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+ }
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+ "cases": [
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+ "pathology": "trauma",
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+ "band": "ectatic",
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+ "study_description": "CT Abdomen Pelvis with Contrast",
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+ "rerun_knobs": "duplicate_scan_perturb",
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+ "stale_until_rerun": true
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+ "row_id": "s1109",
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+ "image_id": "s1109",
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+ "study_description": "CT Abdomen Pelvis without Contrast",
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+ "stale_until_rerun": true
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+ "band": "normal",
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+ "band": "normal",
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+ {
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+ "row_id": "s1102",
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+ {
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+ "row_id": "s0216",
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2013
+ "age": 93.0,
2014
+ "study_type": "ct spine",
2015
+ "pathology": "unclear",
2016
+ "split": "train",
2017
+ "institute": "b",
2018
+ "band_reference": "normal",
2019
+ "band": "normal",
2020
+ "diameter_mm": 24.70323865407125,
2021
+ "contrast_phase": "non-contrast",
2022
+ "study_description": "CT Abdomen Pelvis without Contrast",
2023
+ "rerun_knobs": "",
2024
+ "stale_until_rerun": false
2025
+ },
2026
+ {
2027
+ "row_id": "s0500",
2028
+ "image_id": "s0500",
2029
+ "patient_key": "s0500",
2030
+ "kind": "index",
2031
+ "copy": 0,
2032
+ "new_patient_id": false,
2033
+ "from": null,
2034
+ "case_id": "272591408469599c",
2035
+ "patient_id": "65ade74e14264942",
2036
+ "age": 49.0,
2037
+ "study_type": "ct neck-thorax-abdomen-pelvis",
2038
+ "pathology": "unclear",
2039
+ "split": "train",
2040
+ "institute": "b",
2041
+ "band_reference": "normal",
2042
+ "band": "normal",
2043
+ "diameter_mm": 20.670026608594387,
2044
+ "contrast_phase": "venous",
2045
+ "study_description": "CT Staging Neck/CAP",
2046
+ "rerun_knobs": "",
2047
+ "stale_until_rerun": false
2048
+ }
2049
+ ],
2050
+ "public_name": "hospital_g",
2051
+ "release_key_fingerprint": "88dd7eeed9a0"
2052
+ }
keys/practice/README.md ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Practice answer keys
2
+
3
+ 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.
4
+
5
+ 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.
6
+
7
+ Patients shared across practice hospitals under different ids: hospital_b & hospital_e: 2
8
+
9
+ ## Measures
10
+
11
+ - `aaa_prev_diff`: AAA prevalence difference (= EMD on the binary label)
12
+ - `size_class_hellinger`: Hellinger distance over aaa_size_class
13
+ - `diameter_w1_mm`: W1 over max_aortic_diameter_mm, mm
14
+ - `age_band_tv`: TV over age band
15
+ - `sex_tv`: TV over sex
16
+ - `phase_tv`: TV over contrast phase
17
+ - `slice_class_tv`: TV over slice_thickness_class
18
+ - `vendor_tv`: TV over scanner_vendor
19
+ - `feature_c2st_auc`: classifier two-sample AUC, aorta HU and log organ volumes
20
+ - `embed_c2st_auc`: classifier two-sample AUC, embedding PCA-32
21
+ - `embed_mmd2`: unbiased MMD^2, RBF, embedding PCA-32
22
+ - `cmi_s_y_given_x`: I(S;Y|X) in nats, Y = aaa, X = baseline predicted diameter
23
+ - `label_pred_w1_mm`: W1 over (label diameter - baseline predicted diameter), mm
24
+ - `dice_gap`: |mean baseline aorta Dice difference|
25
+ - `aaa_balacc_gap`: |baseline AAA balanced accuracy difference|
26
+ - `shortcut_max_phi`: max phi between one study_description or protocol_name value and aaa (values on 3 or more rows)
27
+ - `shortcut_u`: I(study_description; aaa) / H(aaa), plug-in (uncertainty coefficient, so prevalence alone does not raise it)
28
+ - `neff_deficit_pid`: 1 - n_eff / n, ICC of diameter by released patient_id
29
+ - `neff_deficit_emb`: 1 - n_eff / n, ICC of diameter by near-duplicate embedding cluster
30
+ - `near_dup_rate`: share of rows with a within-hospital embedding neighbour closer than 0.25
keys/practice/hospital_a.json ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "findings": [
3
+ {
4
+ "description": "Stratified random sample, one scan per patient. The IID control and the reference for every other hospital.",
5
+ "evidence_expected": "Measures sit inside their split-half nulls; no axis stands out.",
6
+ "kind": "none",
7
+ "severity": "none"
8
+ }
9
+ ],
10
+ "hospital": "hospital_a"
11
+ }
keys/practice/hospital_b.json ADDED
@@ -0,0 +1,143 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "findings": [
3
+ {
4
+ "description": "Many patients scanned repeatedly, some under fresh IDs. Looks like 80 cases; holds far fewer independent ones.",
5
+ "evidence_expected": "Near-duplicate embeddings within the hospital, ICC and design effect, n_eff well below n, naive CIs too narrow.",
6
+ "kind": "clustering",
7
+ "severity": "strong"
8
+ },
9
+ {
10
+ "description": "A handful of patients also appear at hospital_e under new IDs.",
11
+ "evidence_expected": "Cross-hospital near-duplicate pairs; a hospital-level split would leak.",
12
+ "kind": "clustering",
13
+ "severity": "weak"
14
+ }
15
+ ],
16
+ "hospital": "hospital_b",
17
+ "reference_measures": {
18
+ "compared_with": "hospital_a",
19
+ "measures": {
20
+ "aaa_balacc_gap": {
21
+ "chance_p95": 0.16666666666666674,
22
+ "chance_p99": 0.2500454545454545,
23
+ "value": 0.015873015873015817,
24
+ "verdict": "within chance"
25
+ },
26
+ "aaa_prev_diff": {
27
+ "chance_p95": 0.08249999999999999,
28
+ "chance_p99": 0.105025,
29
+ "value": 0.0025000000000000022,
30
+ "verdict": "within chance"
31
+ },
32
+ "age_band_tv": {
33
+ "chance_p95": 0.2251249999999999,
34
+ "chance_p99": 0.25749999999999995,
35
+ "value": 0.10750000000000001,
36
+ "verdict": "within chance"
37
+ },
38
+ "cmi_s_y_given_x": {
39
+ "chance_p95": 0.0017412381135585482,
40
+ "chance_p99": 0.00344862445271839,
41
+ "value": -0.0003646204882545065,
42
+ "verdict": "within chance"
43
+ },
44
+ "diameter_w1_mm": {
45
+ "chance_p95": 2.222422553198033,
46
+ "chance_p99": 3.0558571029693504,
47
+ "value": 1.6583997089766074,
48
+ "verdict": "within chance"
49
+ },
50
+ "dice_gap": {
51
+ "chance_p95": 0.00622128738602638,
52
+ "chance_p99": 0.007824171785814063,
53
+ "value": 0.004095622399570842,
54
+ "verdict": "within chance"
55
+ },
56
+ "embed_c2st_auc": {
57
+ "chance_p95": 0.5851531249999999,
58
+ "chance_p99": 0.624419375,
59
+ "value": 0.64425,
60
+ "verdict": "weak"
61
+ },
62
+ "embed_mmd2": {
63
+ "chance_p95": 0.003110258204608671,
64
+ "chance_p99": 0.004315590781960012,
65
+ "value": 0.008291255671871944,
66
+ "verdict": "weak"
67
+ },
68
+ "feature_c2st_auc": {
69
+ "chance_p95": 0.5812312499999999,
70
+ "chance_p99": 0.61777375,
71
+ "value": 0.385625,
72
+ "verdict": "within chance"
73
+ },
74
+ "label_pred_w1_mm": {
75
+ "chance_p95": 0.464911853101532,
76
+ "chance_p99": 0.5753052952284602,
77
+ "value": 0.32971442293168646,
78
+ "verdict": "within chance"
79
+ },
80
+ "near_dup_rate": {
81
+ "chance_p95": 0.0,
82
+ "chance_p99": 0.0,
83
+ "value": 0.75,
84
+ "verdict": "strong"
85
+ },
86
+ "neff_deficit_emb": {
87
+ "chance_p95": 0.0,
88
+ "chance_p99": 0.0,
89
+ "value": 0.5988606323301549,
90
+ "verdict": "strong"
91
+ },
92
+ "neff_deficit_pid": {
93
+ "chance_p95": 0.0,
94
+ "chance_p99": 0.0,
95
+ "value": 0.3837465103580684,
96
+ "verdict": "strong"
97
+ },
98
+ "phase_tv": {
99
+ "chance_p95": 0.14512499999999992,
100
+ "chance_p99": 0.1751499999999999,
101
+ "value": 0.06000000000000001,
102
+ "verdict": "within chance"
103
+ },
104
+ "sex_tv": {
105
+ "chance_p95": 0.15,
106
+ "chance_p99": 0.1725,
107
+ "value": 0.014999999999999958,
108
+ "verdict": "within chance"
109
+ },
110
+ "shortcut_max_phi": {
111
+ "chance_p95": 0.8056815791722832,
112
+ "chance_p99": 0.8885233166386386,
113
+ "value": 0.3391533656562534,
114
+ "verdict": "within chance"
115
+ },
116
+ "shortcut_u": {
117
+ "chance_p95": 0.9368951662102973,
118
+ "chance_p99": 1.0000000000000002,
119
+ "value": 0.5441851217481756,
120
+ "verdict": "within chance"
121
+ },
122
+ "size_class_hellinger": {
123
+ "chance_p95": 0.1501256242747197,
124
+ "chance_p99": 0.16974357552479452,
125
+ "value": 0.10241327124318884,
126
+ "verdict": "within chance"
127
+ },
128
+ "slice_class_tv": {
129
+ "chance_p95": 0.1551249999999999,
130
+ "chance_p99": 0.21764999999999984,
131
+ "value": 0.135,
132
+ "verdict": "within chance"
133
+ },
134
+ "vendor_tv": {
135
+ "chance_p95": 0.0857766323024055,
136
+ "chance_p99": 0.12228137651821862,
137
+ "value": 0.16695675458562056,
138
+ "verdict": "weak"
139
+ }
140
+ },
141
+ "n": 80
142
+ }
143
+ }
keys/practice/hospital_c.json ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "findings": [
3
+ {
4
+ "description": "Thick slices and venous phase only. Same patients-on-paper as the reference, scanned differently.",
5
+ "evidence_expected": "Label prior and demographics match the reference (EMD and Hellinger near zero) while slice thickness and the embeddings shift clearly and baseline Dice drops a little. Aorta HU moves only slightly.",
6
+ "kind": "acquisition",
7
+ "severity": "strong"
8
+ }
9
+ ],
10
+ "hospital": "hospital_c",
11
+ "reference_measures": {
12
+ "compared_with": "hospital_a",
13
+ "measures": {
14
+ "aaa_balacc_gap": {
15
+ "chance_p95": 0.16666666666666674,
16
+ "chance_p99": 0.2500454545454545,
17
+ "value": 0.015873015873015817,
18
+ "verdict": "within chance"
19
+ },
20
+ "aaa_prev_diff": {
21
+ "chance_p95": 0.08249999999999999,
22
+ "chance_p99": 0.105025,
23
+ "value": 0.0025000000000000022,
24
+ "verdict": "within chance"
25
+ },
26
+ "age_band_tv": {
27
+ "chance_p95": 0.2251249999999999,
28
+ "chance_p99": 0.25749999999999995,
29
+ "value": 0.012500000000000002,
30
+ "verdict": "within chance"
31
+ },
32
+ "cmi_s_y_given_x": {
33
+ "chance_p95": 0.0017412381135585482,
34
+ "chance_p99": 0.00344862445271839,
35
+ "value": -0.0008128277899579761,
36
+ "verdict": "within chance"
37
+ },
38
+ "diameter_w1_mm": {
39
+ "chance_p95": 2.222422553198033,
40
+ "chance_p99": 3.0558571029693504,
41
+ "value": 0.7667986187448365,
42
+ "verdict": "within chance"
43
+ },
44
+ "dice_gap": {
45
+ "chance_p95": 0.00622128738602638,
46
+ "chance_p99": 0.007824171785814063,
47
+ "value": 0.013440861705116935,
48
+ "verdict": "weak"
49
+ },
50
+ "embed_c2st_auc": {
51
+ "chance_p95": 0.5851531249999999,
52
+ "chance_p99": 0.624419375,
53
+ "value": 0.757875,
54
+ "verdict": "strong"
55
+ },
56
+ "embed_mmd2": {
57
+ "chance_p95": 0.003110258204608671,
58
+ "chance_p99": 0.004315590781960012,
59
+ "value": 0.025361603605288163,
60
+ "verdict": "strong"
61
+ },
62
+ "feature_c2st_auc": {
63
+ "chance_p95": 0.5812312499999999,
64
+ "chance_p99": 0.61777375,
65
+ "value": 0.610625,
66
+ "verdict": "borderline"
67
+ },
68
+ "label_pred_w1_mm": {
69
+ "chance_p95": 0.464911853101532,
70
+ "chance_p99": 0.5753052952284602,
71
+ "value": 0.26524056538777296,
72
+ "verdict": "within chance"
73
+ },
74
+ "near_dup_rate": {
75
+ "chance_p95": 0.0,
76
+ "chance_p99": 0.0,
77
+ "value": 0.0,
78
+ "verdict": "within chance"
79
+ },
80
+ "neff_deficit_emb": {
81
+ "chance_p95": 0.0,
82
+ "chance_p99": 0.0,
83
+ "value": 0.0,
84
+ "verdict": "within chance"
85
+ },
86
+ "neff_deficit_pid": {
87
+ "chance_p95": 0.0,
88
+ "chance_p99": 0.0,
89
+ "value": 0.0,
90
+ "verdict": "within chance"
91
+ },
92
+ "phase_tv": {
93
+ "chance_p95": 0.14512499999999992,
94
+ "chance_p99": 0.1751499999999999,
95
+ "value": 0.4,
96
+ "verdict": "strong"
97
+ },
98
+ "sex_tv": {
99
+ "chance_p95": 0.15,
100
+ "chance_p99": 0.1725,
101
+ "value": 0.0024999999999999745,
102
+ "verdict": "within chance"
103
+ },
104
+ "shortcut_max_phi": {
105
+ "chance_p95": 0.8056815791722832,
106
+ "chance_p99": 0.8885233166386386,
107
+ "value": 0.2477308382496682,
108
+ "verdict": "within chance"
109
+ },
110
+ "shortcut_u": {
111
+ "chance_p95": 0.9368951662102973,
112
+ "chance_p99": 1.0000000000000002,
113
+ "value": 0.5863806087251241,
114
+ "verdict": "within chance"
115
+ },
116
+ "size_class_hellinger": {
117
+ "chance_p95": 0.1501256242747197,
118
+ "chance_p99": 0.16974357552479452,
119
+ "value": 0.021967824367345092,
120
+ "verdict": "within chance"
121
+ },
122
+ "slice_class_tv": {
123
+ "chance_p95": 0.1551249999999999,
124
+ "chance_p99": 0.21764999999999984,
125
+ "value": 0.89,
126
+ "verdict": "strong"
127
+ },
128
+ "vendor_tv": {
129
+ "chance_p95": 0.0857766323024055,
130
+ "chance_p99": 0.12228137651821862,
131
+ "value": 0.02654639175257731,
132
+ "verdict": "within chance"
133
+ }
134
+ },
135
+ "n": 80
136
+ }
137
+ }
keys/practice/hospital_d.json ADDED
@@ -0,0 +1,143 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "findings": [
3
+ {
4
+ "description": "Far more aneurysms than the reference, as at a specialist vascular practice.",
5
+ "evidence_expected": "Higher AAA prevalence and a diameter distribution shifted right (W1 in mm).",
6
+ "kind": "label_prior",
7
+ "severity": "strong"
8
+ },
9
+ {
10
+ "description": "Older and more male, moving together with prevalence and more arterial-phase imaging.",
11
+ "evidence_expected": "The honest answer names the confounding and says what cannot be separated, rather than claiming a clean decomposition.",
12
+ "kind": "population",
13
+ "severity": "strong"
14
+ }
15
+ ],
16
+ "hospital": "hospital_d",
17
+ "reference_measures": {
18
+ "compared_with": "hospital_a",
19
+ "measures": {
20
+ "aaa_balacc_gap": {
21
+ "chance_p95": 0.16666666666666674,
22
+ "chance_p99": 0.2500454545454545,
23
+ "value": 0.002777777777777768,
24
+ "verdict": "within chance"
25
+ },
26
+ "aaa_prev_diff": {
27
+ "chance_p95": 0.08249999999999999,
28
+ "chance_p99": 0.105025,
29
+ "value": 0.16,
30
+ "verdict": "weak"
31
+ },
32
+ "age_band_tv": {
33
+ "chance_p95": 0.2251249999999999,
34
+ "chance_p99": 0.25749999999999995,
35
+ "value": 0.4175,
36
+ "verdict": "strong"
37
+ },
38
+ "cmi_s_y_given_x": {
39
+ "chance_p95": 0.0017412381135585482,
40
+ "chance_p99": 0.00344862445271839,
41
+ "value": -0.00029198949655167563,
42
+ "verdict": "within chance"
43
+ },
44
+ "diameter_w1_mm": {
45
+ "chance_p95": 2.222422553198033,
46
+ "chance_p99": 3.0558571029693504,
47
+ "value": 6.522980006480996,
48
+ "verdict": "strong"
49
+ },
50
+ "dice_gap": {
51
+ "chance_p95": 0.00622128738602638,
52
+ "chance_p99": 0.007824171785814063,
53
+ "value": 0.0027557173710358596,
54
+ "verdict": "within chance"
55
+ },
56
+ "embed_c2st_auc": {
57
+ "chance_p95": 0.5851531249999999,
58
+ "chance_p99": 0.624419375,
59
+ "value": 0.61225,
60
+ "verdict": "borderline"
61
+ },
62
+ "embed_mmd2": {
63
+ "chance_p95": 0.003110258204608671,
64
+ "chance_p99": 0.004315590781960012,
65
+ "value": 0.007755568412725511,
66
+ "verdict": "weak"
67
+ },
68
+ "feature_c2st_auc": {
69
+ "chance_p95": 0.5812312499999999,
70
+ "chance_p99": 0.61777375,
71
+ "value": 0.76075,
72
+ "verdict": "strong"
73
+ },
74
+ "label_pred_w1_mm": {
75
+ "chance_p95": 0.464911853101532,
76
+ "chance_p99": 0.5753052952284602,
77
+ "value": 0.5700975585828539,
78
+ "verdict": "borderline"
79
+ },
80
+ "near_dup_rate": {
81
+ "chance_p95": 0.0,
82
+ "chance_p99": 0.0,
83
+ "value": 0.0,
84
+ "verdict": "within chance"
85
+ },
86
+ "neff_deficit_emb": {
87
+ "chance_p95": 0.0,
88
+ "chance_p99": 0.0,
89
+ "value": 0.0,
90
+ "verdict": "within chance"
91
+ },
92
+ "neff_deficit_pid": {
93
+ "chance_p95": 0.0,
94
+ "chance_p99": 0.0,
95
+ "value": 0.0,
96
+ "verdict": "within chance"
97
+ },
98
+ "phase_tv": {
99
+ "chance_p95": 0.14512499999999992,
100
+ "chance_p99": 0.1751499999999999,
101
+ "value": 0.5599999999999999,
102
+ "verdict": "strong"
103
+ },
104
+ "sex_tv": {
105
+ "chance_p95": 0.15,
106
+ "chance_p99": 0.1725,
107
+ "value": 0.18999999999999997,
108
+ "verdict": "weak"
109
+ },
110
+ "shortcut_max_phi": {
111
+ "chance_p95": 0.8056815791722832,
112
+ "chance_p99": 0.8885233166386386,
113
+ "value": 0.48507125007266594,
114
+ "verdict": "within chance"
115
+ },
116
+ "shortcut_u": {
117
+ "chance_p95": 0.9368951662102973,
118
+ "chance_p99": 1.0000000000000002,
119
+ "value": 0.5317518337835133,
120
+ "verdict": "within chance"
121
+ },
122
+ "size_class_hellinger": {
123
+ "chance_p95": 0.1501256242747197,
124
+ "chance_p99": 0.16974357552479452,
125
+ "value": 0.16829488599721648,
126
+ "verdict": "borderline"
127
+ },
128
+ "slice_class_tv": {
129
+ "chance_p95": 0.1551249999999999,
130
+ "chance_p99": 0.21764999999999984,
131
+ "value": 0.1275,
132
+ "verdict": "within chance"
133
+ },
134
+ "vendor_tv": {
135
+ "chance_p95": 0.0857766323024055,
136
+ "chance_p99": 0.12228137651821862,
137
+ "value": 0.04850647634152787,
138
+ "verdict": "within chance"
139
+ }
140
+ },
141
+ "n": 80
142
+ }
143
+ }
keys/practice/hospital_e.json ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "findings": [
3
+ {
4
+ "description": "Measures the lumen rather than the outer wall and calls AAA at 2.5 cm. Same images, different truth.",
5
+ "evidence_expected": "P(Y) and P(X) match the reference, but the image-to-label relationship differs (I(S;Y|X) > 0; diameter vs image features disagree on matched cases).",
6
+ "kind": "labeling_convention",
7
+ "severity": "strong"
8
+ }
9
+ ],
10
+ "hospital": "hospital_e",
11
+ "reference_measures": {
12
+ "compared_with": "hospital_a",
13
+ "measures": {
14
+ "aaa_balacc_gap": {
15
+ "chance_p95": 0.16666666666666674,
16
+ "chance_p99": 0.2500454545454545,
17
+ "value": 0.4444444444444444,
18
+ "verdict": "weak"
19
+ },
20
+ "aaa_prev_diff": {
21
+ "chance_p95": 0.08249999999999999,
22
+ "chance_p99": 0.105025,
23
+ "value": 0.0025000000000000022,
24
+ "verdict": "within chance"
25
+ },
26
+ "age_band_tv": {
27
+ "chance_p95": 0.2251249999999999,
28
+ "chance_p99": 0.25749999999999995,
29
+ "value": 0.017500000000000005,
30
+ "verdict": "within chance"
31
+ },
32
+ "cmi_s_y_given_x": {
33
+ "chance_p95": 0.0017412381135585482,
34
+ "chance_p99": 0.00344862445271839,
35
+ "value": 0.0197012712436091,
36
+ "verdict": "strong"
37
+ },
38
+ "diameter_w1_mm": {
39
+ "chance_p95": 2.222422553198033,
40
+ "chance_p99": 3.0558571029693504,
41
+ "value": 1.997859351480174,
42
+ "verdict": "within chance"
43
+ },
44
+ "dice_gap": {
45
+ "chance_p95": 0.00622128738602638,
46
+ "chance_p99": 0.007824171785814063,
47
+ "value": 0.0050096979466958125,
48
+ "verdict": "within chance"
49
+ },
50
+ "embed_c2st_auc": {
51
+ "chance_p95": 0.5851531249999999,
52
+ "chance_p99": 0.624419375,
53
+ "value": 0.52125,
54
+ "verdict": "within chance"
55
+ },
56
+ "embed_mmd2": {
57
+ "chance_p95": 0.003110258204608671,
58
+ "chance_p99": 0.004315590781960012,
59
+ "value": -0.0018771111598774315,
60
+ "verdict": "within chance"
61
+ },
62
+ "feature_c2st_auc": {
63
+ "chance_p95": 0.5812312499999999,
64
+ "chance_p99": 0.61777375,
65
+ "value": 0.569125,
66
+ "verdict": "within chance"
67
+ },
68
+ "label_pred_w1_mm": {
69
+ "chance_p95": 0.464911853101532,
70
+ "chance_p99": 0.5753052952284602,
71
+ "value": 0.6040347949900364,
72
+ "verdict": "weak"
73
+ },
74
+ "near_dup_rate": {
75
+ "chance_p95": 0.0,
76
+ "chance_p99": 0.0,
77
+ "value": 0.0,
78
+ "verdict": "within chance"
79
+ },
80
+ "neff_deficit_emb": {
81
+ "chance_p95": 0.0,
82
+ "chance_p99": 0.0,
83
+ "value": 0.0,
84
+ "verdict": "within chance"
85
+ },
86
+ "neff_deficit_pid": {
87
+ "chance_p95": 0.0,
88
+ "chance_p99": 0.0,
89
+ "value": 0.0,
90
+ "verdict": "within chance"
91
+ },
92
+ "phase_tv": {
93
+ "chance_p95": 0.14512499999999992,
94
+ "chance_p99": 0.1751499999999999,
95
+ "value": 0.26,
96
+ "verdict": "weak"
97
+ },
98
+ "sex_tv": {
99
+ "chance_p95": 0.15,
100
+ "chance_p99": 0.1725,
101
+ "value": 0.0024999999999999745,
102
+ "verdict": "within chance"
103
+ },
104
+ "shortcut_max_phi": {
105
+ "chance_p95": 0.8056815791722832,
106
+ "chance_p99": 0.8885233166386386,
107
+ "value": 0.2842427860477457,
108
+ "verdict": "within chance"
109
+ },
110
+ "shortcut_u": {
111
+ "chance_p95": 0.9368951662102973,
112
+ "chance_p99": 1.0000000000000002,
113
+ "value": 0.3588030275715051,
114
+ "verdict": "within chance"
115
+ },
116
+ "size_class_hellinger": {
117
+ "chance_p95": 0.1501256242747197,
118
+ "chance_p99": 0.16974357552479452,
119
+ "value": 0.10241327124318884,
120
+ "verdict": "within chance"
121
+ },
122
+ "slice_class_tv": {
123
+ "chance_p95": 0.1551249999999999,
124
+ "chance_p99": 0.21764999999999984,
125
+ "value": 0.0025000000000000022,
126
+ "verdict": "within chance"
127
+ },
128
+ "vendor_tv": {
129
+ "chance_p95": 0.0857766323024055,
130
+ "chance_p99": 0.12228137651821862,
131
+ "value": 0.060028709382748284,
132
+ "verdict": "within chance"
133
+ }
134
+ },
135
+ "n": 80
136
+ }
137
+ }