Datasets:
metadata
language:
- en
license: mit
pretty_name: Clinical Healing Trajectory Tokenization and Phase Segmentation v0.1
dataset_name: clinical-healing-trajectory-tokenization-phase-segmentation-v0.1
tags:
- clarusc64
- clinical
- recovery
- sepsis
- surgery
- trauma
- temporal-logic
- phase-segmentation
task_categories:
- text-classification
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/train.csv
- split: test
path: data/test.csv
What this dataset tests
Whether a model can segment high-frequency recovery data
into interpretable healing phases.
Required outputs
- phase_sequence
- phase_boundaries
- phase_confidence_0_100
Token labels
- acute_drop
- early_rebound
- consolidation_plateau
- oscillatory_instability
- secondary_drop
- delayed_rebound
- steady_ascent
- maladaptive_plateau
- recovery_lock_in
Boundary format
Use day indices
example
acute_drop d0-d2
Typical failures
- naming phases without boundaries
- using vague labels like "improving"
- missing secondary drops and rebounds
Suggested prompt wrapper
System
You tokenize healing trajectories into phases.
User
Insult type
{insult_type}
High-frequency summary
{high_frequency_summary}
Return
- phase sequence using tokens separated by ->
- phase boundaries as token dX-dY
- confidence score 0-100
- one sentence evidence
Citation
ClarusC64 dataset family