Datasets:

ClarusC64
/

clinical-quad-surgical-stress-buffer-lag-coupling-postop-collapse-v0.8

scenario_id float64	surgical_stress float64	physiologic_buffer float64	recovery_lag float64	system_coupling float64	drift_gradient float64	drift_velocity float64	drift_acceleration float64	boundary_distance float64	secondary_boundary_distance float64	boundary_competition_ratio float64	boundary_uncertainty float64	trajectory_uncertainty float64	regime_confidence float64	regime_transition_score string	transition_direction float64	regime_separation_margin float64	transition_uncertainty float64	transition_velocity float64	perturbation_radius float64	collapse_trigger string	recovery_distance float64	recovery_gradient float64	return_feasibility float64	delta_surgical_stress float64	delta_physiologic_buffer float64	delta_recovery_lag float64	delta_system_coupling float64	trajectory_shift float64	minimal_intervention_path string	stabilization_success int64	label_postop_collapse int64	__index_level_0__ string
0.84	0.31	0.72	0.76	0.48	0.42	0.21	0.17	0.21	1.24	0.13	0.17	0.64	0.61	inflammation_to_hemodynamic_collapse	0.11	0.16	0.24	0.29	0.28	occult_bleeding	0.25	-0.15	0.58	-0.13	0.11	-0.1	-0.09	-0.16	fluid resuscitation; early transfusion; return to theatre review	1	1	POSTOP001
0.89	0.22	0.81	0.84	0.66	0.57	0.35	0.1	0.11	1.1	0.19	0.23	0.48	0.78	hypovolemia_to_septic_collapse	0.07	0.22	0.33	0.37	0.35	anastomotic_leak	0.36	-0.04	0.27	-0.05	0.02	-0.03	-0.02	-0.06	broad-spectrum antibiotics; source control imaging; ICU review	0	0	POSTOP002
0.77	0.39	0.63	0.68	0.29	0.24	0.12	0.25	0.39	1.56	0.1	0.13	0.73	0.34	inflammation_to_ileus_decompensation	0.18	0.12	0.15	0.2	0.21	persistent_pain_stress	0.2	-0.13	0.69	-0.1	0.09	-0.08	-0.07	-0.14	optimize analgesia; mobilization; electrolyte correction	1	1	POSTOP003
0.92	0.18	0.86	0.88	0.74	0.63	0.42	0.08	0.09	1.12	0.23	0.28	0.41	0.84	hypovolemia_to_multiorgan_collapse	0.06	0.27	0.41	0.4	0.39	uncontrolled_hemorrhage	0.41	-0.02	0.19	-0.03	0.01	-0.01	0	-0.03	massive transfusion protocol; operative control; vasopressor bridge	0	0	POSTOP004
0.71	0.45	0.55	0.6	0.21	0.18	0.07	0.33	0.51	1.55	0.08	0.1	0.81	0.25	stable_recovery_to_slow_decompensation	0.22	0.09	0.1	0.16	0.17	fluid_shift	0.17	-0.11	0.76	-0.08	0.07	-0.06	-0.05	-0.12	maintenance fluids; electrolyte review; routine escalation watch	1	1	POSTOP005
0.86	0.26	0.78	0.82	0.58	0.5	0.29	0.13	0.14	1.08	0.16	0.21	0.55	0.76	inflammation_to_hemodynamic_collapse	0.08	0.2	0.31	0.33	0.31	vasodilatory_instability	0.31	-0.05	0.34	-0.05	0.03	-0.03	-0.01	-0.07	vasopressor support; fluids; serial lactate review	0	0	POSTOP006
0.74	0.41	0.59	0.64	0.25	0.21	0.09	0.28	0.34	1.21	0.11	0.12	0.77	0.47	inflammation_to_ileus_decompensation	0.14	0.13	0.17	0.22	0.19	gut_dysmotility	0.19	-0.14	0.7	-0.09	0.08	-0.07	-0.06	-0.15	bowel rest; decompression; electrolyte optimization	1	1	POSTOP007
0.94	0.16	0.89	0.91	0.8	0.68	0.47	0.06	0.07	1.17	0.25	0.3	0.36	0.88	hypovolemia_to_multiorgan_collapse	0.05	0.29	0.46	0.43	0.43	refractory_shock	0.44	-0.01	0.15	-0.01	0	0	0.01	0	damage control strategy; ICU transfer; invasive monitoring	0	0	POSTOP008
0.69	0.47	0.53	0.57	0.18	0.15	0.05	0.36	0.54	1.5	0.07	0.09	0.83	0.19	stable_recovery_to_slow_decompensation	0.24	0.08	0.08	0.15	0.15	postop_fluid_imbalance	0.14	-0.12	0.79	-0.07	0.06	-0.05	-0.05	-0.13	watchful monitoring; fluid balance charting; nurse escalation protocol	1	1	POSTOP009

What this repo does

This repository provides a Clarus v0.8 clinical quad dataset for detecting and reasoning about postoperative collapse regime transitions.

The dataset models situations where a patient state is no longer contained within a single postoperative recovery basin but is shifting between competing regimes such as:

inflammatory postoperative stress
hemodynamic collapse
septic deterioration
multiorgan instability

This is the conceptual upgrade introduced in Clarus v0.8.

Earlier ladder versions detect instability, forecast deterioration, estimate collapse boundaries, model recovery geometry, and reason about intervention.

v0.8 introduces regime transition geometry.

The system now measures not only distance to the nearest failure boundary but also distance to the nearest competing regime boundary.

This allows models to detect:

regime switching
competing failure modes
unstable regime identity
transition-aware intervention reasoning

Core quad

The four core variables in this dataset are:

surgical_stress
physiologic_buffer
recovery_lag
system_coupling

Operational definitions:

surgical_stress

Acute operative burden and inflammatory load imposed by surgery and its complications.

physiologic_buffer

Remaining adaptive reserve available to tolerate blood loss, inflammation, infection, and metabolic demand.

recovery_lag

Delay in returning toward expected postoperative stabilization.

system_coupling

Degree to which deterioration in one postoperative subsystem is propagating into broader multisystem instability.

Clinical variable mapping

Quad Variable	Clinical Measurements	Typical Indicators
surgical_stress	Operative duration, blood loss, CRP, lactate	Long case, high blood loss, rising CRP, lactate elevation
physiologic_buffer	Hemoglobin reserve, albumin, frailty, cardiopulmonary reserve	Anemia, low albumin, frailty, poor reserve
recovery_lag	Time to mobilization, bowel recovery, MAP normalization	Delayed mobilization, ileus, prolonged hypotension
system_coupling	Creatinine rise, oxygen requirement, SOFA score	Rising creatinine, oxygen escalation, SOFA increase

These mappings are illustrative rather than prescriptive.

The dataset encodes structural system state, not a single clinical protocol.

Why the second boundary matters

Most instability detectors estimate only one quantity:

distance to the nearest collapse boundary.

This is insufficient for real postoperative deterioration.

A patient may appear to be deepening within one recovery complication while the deeper reality is that they are crossing into a different failure basin.

v0.8 introduces:

secondary_boundary_distance

The model now measures:

distance to the current regime boundary
distance to the competing regime boundary

This enables detection of regime competition.

When the two distances converge, regime identity becomes unstable.

The derived signal

boundary_competition_ratio

secondary_boundary_distance / boundary_distance

acts as a marker of this instability.

Values approaching 1 indicate a system positioned between regimes.

Structural Note

This dataset is part of the Clarus ladder.

The ladder reconstructs instability geometry step by step:

v0.1 cascade detection
v0.2 trajectory awareness
v0.3 cascade forecasting
v0.4 boundary discovery
v0.5 recovery geometry
v0.6 intervention reasoning
v0.7 uncertainty-aware intervention
v0.8 regime transition geometry

Each rung increases resolution of the system state space.

v0.8 is the first rung where competition between instability basins is explicitly modeled.

That upgrade transforms Clarus from a collapse detector into a phase-transition instrument for complex systems.

Example regime competition row

A typical v0.8 postoperative transition scenario looks like this.

Field	Example
surgical_stress	0.89
physiologic_buffer	0.22
recovery_lag	0.81
system_coupling	0.84
boundary_distance	0.10
secondary_boundary_distance	0.11
boundary_competition_ratio	1.10
regime_transition_score	0.78
transition_direction	hypovolemia_to_septic_collapse
regime_separation_margin	0.07
transition_uncertainty	0.22
transition_velocity	0.33
collapse_trigger	anastomotic_leak

Interpretation:

The patient is close to the current regime boundary while a competing regime boundary is also nearby.

The system is transitioning from one unstable postoperative state into another more dangerous basin.

Prediction target

The prediction target is:

label_postop_collapse

Default label rule:

label = 1 if stabilization_success = 1 and trajectory_shift < -0.10

Relaxed variant:

label = 1 if stabilization_success = 1

This preserves consistency with v0.6 and v0.7 while extending the geometry into v0.8 regime transition reasoning.

Row structure

Each dataset row contains layered signals.

Core state

surgical_stress
physiologic_buffer
recovery_lag
system_coupling

Trajectory signals

drift_gradient
drift_velocity
drift_acceleration

Boundary geometry

boundary_distance
secondary_boundary_distance
boundary_competition_ratio

Uncertainty layer

boundary_uncertainty
trajectory_uncertainty
regime_confidence

Regime transition layer

regime_transition_score
transition_direction
regime_separation_margin
transition_uncertainty
transition_velocity

Perturbation and collapse markers

perturbation_radius
collapse_trigger

Recovery geometry

recovery_distance
recovery_gradient
return_feasibility

Intervention vector

delta_surgical_stress
delta_physiologic_buffer
delta_recovery_lag
delta_system_coupling

Outcome fields

trajectory_shift
minimal_intervention_path
stabilization_success
label_postop_collapse in train only

tester.csv excludes:

stabilization_success
label_postop_collapse

Files

data/train.csv

Training rows including outcome labels.

data/tester.csv

Evaluation rows with labels removed.

scorer.py

Reference v0.8 evaluation script.

Returns:

Classification metrics

accuracy
precision
recall
f1
confusion matrix

Regime transition diagnostics

recall_regime_transition_detection
false_stable_regime_rate
transition_direction_accuracy
high_uncertainty_transition_miss_rate
transition_detection_accuracy
false_transition_rate
missed_transition_rate
boundary_competition_error
misidentified_primary_regime_rate

Additional information

support counts
threshold transparency
dynamic label column discovery

benchmark_spec.json

Machine-readable benchmark contract describing task type, target label, withheld fields, metric definitions, thresholds, and prediction-file requirements.

Evaluation

Primary metrics

recall_regime_transition_detection

false_stable_regime_rate

Transition diagnostics

transition_direction_accuracy
high_uncertainty_transition_miss_rate
missed_transition_rate
boundary_competition_error
misidentified_primary_regime_rate

Additional reference diagnostics

transition_detection_accuracy
false_transition_rate

The goal of evaluation is not only to test whether a model predicts the correct label.

It tests whether the model correctly detects regime transitions and their geometry.

Note on transition_velocity

transition_velocity represents the rate at which the system moves toward the competing basin.

The variable is included in the dataset because it can help models reason about escalation speed.

The reference scorer does not yet evaluate this field directly.

Future versions may introduce velocity-aware diagnostics.

Production deployment

This dataset class functions best as a transition-aware monitoring layer rather than a standalone classifier.

Typical deployments include:

surgical ward deterioration monitoring
enhanced recovery pathway oversight
ICU escalation support
digital twin modeling of postoperative instability
AI model auditing for competing failure-mode recognition

The value lies in representing where the patient sits between competing basins.

Enterprise and research collaboration

This dataset class is suitable for collaboration with:

perioperative analytics teams
surgical outcomes research groups
critical care teams
clinical AI safety researchers
digital twin developers
early-warning and escalation platforms

The framework provides explicit representation of:

which regime a patient is leaving
which regime is emerging
how uncertain the transition is
whether stabilization remains structurally feasible

License

MIT

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