Datasets:

ClarusC64
/

ffr-subgroup-failure-surface-routing-v0.1

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'__index_level_0__'})

This happened while the csv dataset builder was generating data using

hf://datasets/ClarusC64/ffr-subgroup-failure-surface-routing-v0.1/data/test.csv (at revision 9b4517132fb531a9e4b96f29cbd5a66f461427ef), [/tmp/hf-datasets-cache/medium/datasets/58105004692090-config-parquet-and-info-ClarusC64-ffr-subgroup-fa-7db82c19/hub/datasets--ClarusC64--ffr-subgroup-failure-surface-routing-v0.1/snapshots/9b4517132fb531a9e4b96f29cbd5a66f461427ef/data/test.csv (origin=hf://datasets/ClarusC64/ffr-subgroup-failure-surface-routing-v0.1@9b4517132fb531a9e4b96f29cbd5a66f461427ef/data/test.csv)]

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              id: string
              case_context: double
              vessel_tortuosity: double
              calcification_burden: int64
              lesion_length_mm: double
              segmentation_confidence: double
              image_artifact_score: double
              ai_ffr_prediction: double
              ai_ffr_run_variance: double
              model_disagreement: double
              invasive_ffr_ground_truth: double
              abs_error: int64
              discordance_flag: string
              discordance_type: string
              subgroup_risk_label: double
              reliability_drop_score: double
              failure_subgroup: double
              predicted_error_range: double
              intervention_route: double
              fallback_protocol: double
              route_priority: double
              confidence_score: double
              notes: string
              constraints: string
              gold_checklist: string
              __index_level_0__: string
              -- schema metadata --
              pandas: '{"index_columns": ["__index_level_0__"], "column_indexes": [{"na' + 3649
              to
              {'id': Value('string'), 'case_context': Value('string'), 'vessel_tortuosity': Value('float64'), 'calcification_burden': Value('float64'), 'lesion_length_mm': Value('int64'), 'segmentation_confidence': Value('float64'), 'image_artifact_score': Value('float64'), 'ai_ffr_prediction': Value('float64'), 'ai_ffr_run_variance': Value('float64'), 'model_disagreement': Value('float64'), 'invasive_ffr_ground_truth': Value('float64'), 'abs_error': Value('float64'), 'discordance_flag': Value('int64'), 'discordance_type': Value('string'), 'subgroup_risk_label': Value('string'), 'reliability_drop_score': Value('float64'), 'failure_subgroup': Value('string'), 'predicted_error_range': Value('string'), 'intervention_route': Value('string'), 'fallback_protocol': Value('string'), 'route_priority': Value('string'), 'confidence_score': Value('float64'), 'notes': Value('string'), 'constraints': Value('string'), 'gold_checklist': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1889, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'__index_level_0__'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/ClarusC64/ffr-subgroup-failure-surface-routing-v0.1/data/test.csv (at revision 9b4517132fb531a9e4b96f29cbd5a66f461427ef), [/tmp/hf-datasets-cache/medium/datasets/58105004692090-config-parquet-and-info-ClarusC64-ffr-subgroup-fa-7db82c19/hub/datasets--ClarusC64--ffr-subgroup-failure-surface-routing-v0.1/snapshots/9b4517132fb531a9e4b96f29cbd5a66f461427ef/data/test.csv (origin=hf://datasets/ClarusC64/ffr-subgroup-failure-surface-routing-v0.1@9b4517132fb531a9e4b96f29cbd5a66f461427ef/data/test.csv)]
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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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	failure_subgroup string	predicted_error_range string	intervention_route string	fallback_protocol string	route_priority string	confidence_score float64	notes string	constraints string	gold_checklist string
SFR-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	none	±0.03	continue	none	none	0.9	safe	<=320 words	subgroup+range+route+fallback+confidence
SFR-002	heavily calcified silent drift	0.4	0.8	26	0.78	0.14	0.76	0.03	0.06	0.67	0.09	1	calcification-driven overestimate	heavily-calcified	0.62	calcification failure surface	±0.10	defer-ai-ffr	invasive FFR recommended	high	0.84	route to invasive	<=320 words	subgroup+range+route+fallback+confidence
SFR-003	high tortuosity instability	0.72	0.25	22	0.72	0.12	0.74	0.05	0.1	0.66	0.08	1	tortuosity-driven instability	high-tortuosity	0.58	tortuosity failure surface	±0.08	segmentation-repair-then-rerun	manual edit + rerun; if discordant invasive	medium	0.82	repair path	<=320 words	subgroup+range+route+fallback+confidence
SFR-004	artifact-amplified discordance	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-driven failure surface	±0.12	rescan-protocol	repeat scan with motion control	critical	0.8	block unreliable scan	<=320 words	subgroup+range+route+fallback+confidence
SFR-005	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	low-confidence failure surface	±0.09	second-model-arbitration	run secondary model; manual review if split	high	0.81	arbitration route	<=320 words	subgroup+range+route+fallback+confidence
SFR-006	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	tolerant subgroup	±0.05	tighten-qa-thresholds	raise artifact gate; monitor variance	low	0.86	keep but monitor	<=320 words	subgroup+range+route+fallback+confidence

Goal

Given a discordance event
route the correct clinical action.

This dataset treats failure
as a subgroup surface.

Not a single bad prediction.

Inputs

anatomy complexity metrics
image artifact signals
AI prediction stability signals
invasive FFR ground truth (for labeling)

Required outputs

failure_subgroup
predicted_error_range
intervention_route
fallback_protocol
confidence_score

Intervention routes

Examples:

continue (safe zone)
tighten QA thresholds
segmentation repair then rerun
re-scan protocol
defer AI-FFR and route to invasive FFR
secondary model arbitration

Why it matters

Validators need a map of:

which subgroup is failing
how big the likely error is
what the safest workflow route is

This makes AI-FFR deployable
with a defined safety envelope.

Evaluation

The scorer checks that the response includes:

subgroup name
numeric error range
route and fallback
confidence score from 0 to 1

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