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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+language:
+  - en
+tags:
+  - healthcare
+  - stroke
+  - cerebrovascular
+  - hypertension
+  - neurology
+  - thrombolysis
+  - sub-saharan-africa
+  - lmic
+pretty_name: "Stroke & Cerebrovascular Disease (Type, Severity, Treatment, Disability)"
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: tertiary_hospital
+    data_files: data/stroke_tertiary_hospital.csv
+  - config_name: district_hospital
+    data_files: data/stroke_district_hospital.csv
+    default: true
+  - config_name: rural_facility
+    data_files: data/stroke_rural_facility.csv
+---
+
+# Stroke & Cerebrovascular Disease Dataset
+
+## Abstract
+
+This dataset provides **30,000 simulated stroke records** (10,000 per scenario) of adults presenting with acute stroke in sub-Saharan Africa. Each record contains 50+ variables including risk factors, stroke type, severity (NIHSS, GCS), imaging, treatment, complications, and outcomes (30-day mortality, mRS). Three settings: tertiary hospital (28% mortality), district hospital (41%), and rural facility (57%).
+
+## 1. Introduction
+
+Stroke in Africa has an annual incidence up to 316/100K person-years, with 30-day case fatality rates of ~30% (range 18-40%) and 90-day mortality reaching 50% at some centres. Approximately 60% of strokes are ischaemic and 32% haemorrhagic — a higher proportion of haemorrhagic stroke than in HICs. Mean age at onset is ~59 years, 10 years younger than Western populations. Hypertension is the dominant risk factor with BP control rates <10% in many SSA populations. CT scanning and thrombolysis are virtually unavailable outside tertiary centres.
+
+**This dataset is entirely simulated. It must not be used for clinical decision-making.**
+
+## 2. Methodology
+
+### 2.1 Parameterization
+
+| Parameter | Value | Source |
+| --- | --- | --- |
+| Incidence (Africa) | Up to 316/100K | Frontiers Stroke 2025 |
+| 30-day CFR (SSA pooled) | ~30% | PLOS Glob PH 2024 |
+| 90-day mortality | ~50% | Muhimbili 2018 |
+| Ischaemic / ICH / SAH | 60% / 32% / 8% | Sierra Leone 2021 |
+| Mean age | 59 years | Sierra Leone 2021 |
+| Hypertension prevalence | 75% | PMC 2015 |
+| BP control rate | <10% | PMC 2015 |
+| Recurrence rate | 9-25% | PMC 2024 |
+
+### 2.2 Scenario Design
+
+| Scenario | CT | Thrombolysis | Stroke Unit | 30-Day Mortality |
+| --- | --- | --- | --- | --- |
+| Tertiary hospital | Yes | Limited | Yes | 28% |
+| District hospital | No | No | No | 41% |
+| Rural facility | No | No | No | 57% |
+
+## 3. Schema
+
+| Column | Type | Description |
+| --- | --- | --- |
+| id | int | Unique identifier |
+| age_years | int | Patient age |
+| sex | categorical | M / F |
+| hypertension | binary | Hypertension |
+| systolic_bp | int | Systolic BP (mmHg) |
+| diastolic_bp | int | Diastolic BP (mmHg) |
+| diabetes | binary | Diabetes |
+| atrial_fibrillation | binary | AF |
+| smoking | binary | Current smoker |
+| alcohol_heavy | binary | Heavy alcohol |
+| prior_stroke | binary | Prior stroke |
+| stroke_type | categorical | ischaemic / intracerebral_haemorrhage / subarachnoid_haemorrhage |
+| ischaemic_subtype | categorical | large_vessel / small_vessel / cardioembolic / undetermined |
+| nihss_score | int | NIHSS (0-42) |
+| gcs_score | int | GCS (3-15) |
+| hemiparesis | binary | Hemiparesis |
+| aphasia | binary | Aphasia |
+| dysphagia | binary | Dysphagia |
+| onset_to_arrival_hours | int | Time to presentation |
+| ct_performed | binary | CT scan done |
+| thrombolysis | binary | Thrombolysis given |
+| antiplatelet | binary | Antiplatelet |
+| statin | binary | Statin |
+| physiotherapy_started | binary | Early physiotherapy |
+| aspiration_pneumonia | binary | Aspiration pneumonia |
+| outcome_30_day | categorical | survived / died |
+| mrs_discharge | int | Modified Rankin Scale (0-5) |
+| disability | categorical | none / mild / moderate / severe |
+| hospital_days | int | Hospital stay |
+
+## 4. Validation
+
+<p align="center">
+  <img src="validation_report.png" alt="Validation Report" width="100%">
+</p>
+
+Key validation checks:
+
+- **Mortality gradient**: 28% → 41% → 57% ✓
+- **Stroke type**: 60% ischaemic, 32% ICH ✓
+- **Hypertension**: 75% prevalence ✓
+- **CT access**: 80% tertiary vs 0% district/rural ✓
+- **ICH mortality > ischaemic** ✓
+- **Mean age ~59** ✓
+
+## 5. Usage
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("electricsheepafrica/stroke-cerebrovascular-disease", "district_hospital")
+df = dataset["train"].to_pandas()
+```
+
+## 6. Limitations
+
+- **Simulated**: Not from real stroke registries.
+- **No neuroimaging**: No actual CT/MRI data.
+- **No longitudinal**: 30-day outcome only, no long-term follow-up.
+- **No rehabilitation**: Limited rehab outcome data.
+- **Simplified**: No detailed vascular territory mapping.
+
+## 7. References
+
+1. Frontiers Stroke (2025). Strategic action plan for stroke in Africa.
+2. PLOS Glob Public Health (2024). 30-day stroke CFR in SSA.
+3. PubMed (2021). Stroke register Sierra Leone.
+4. PubMed (2020). Predictors of stroke mortality Uganda.
+5. PubMed (2018). Stroke at Muhimbili Tanzania.
+6. PMC (2015). Burden of stroke in Africa.
+7. PMC (2024). Stroke recurrence in SSA.
+
+## Citation
+
+```bibtex
+@dataset{esa_stroke_2025,
+  title={Stroke and Cerebrovascular Disease Dataset},
+  author={Electric Sheep Africa},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/electricsheepafrica/stroke-cerebrovascular-disease}
+}
+```
+
+## License
+
+[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)

generate_dataset.py

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+#!/usr/bin/env python3
+"""
+Literature-Informed Stroke & Cerebrovascular Disease Dataset
+==============================================================
+
+Generates realistic synthetic records of stroke patients in
+sub-Saharan Africa, including risk factors, stroke type, severity,
+imaging, treatment, and outcomes.
+
+Target population: Adults presenting with acute stroke to SSA
+health facilities.
+
+References (web-searched):
+-----------
+[1]  Frontiers Stroke 2025. Africa stroke incidence up to
+     316/100K person-years. 3-year fatality rate reaching 84%.
+[2]  PLOS Glob Public Health 2024. 30-day in-hospital stroke
+     CFR in SSA: pooled ~30% (range 18-40%).
+[3]  Sierra Leone stroke register 2021. 60% ischaemic, 22%
+     ICH, 3% SAH, 15% undetermined. Mean age 59.
+[4]  Uganda cohort 2020. Low consciousness at admission,
+     severity, haemorrhagic type predict 30/90-day mortality.
+[5]  Muhimbili Tanzania 2018. 90-day mortality 50%.
+     Thrombolysis virtually unavailable in SSA.
+[6]  PMC 2015. Hypertension is #1 risk factor. BP control
+     rates <10% in many SSA populations.
+[7]  Stroke recurrence SSA 2024. Recurrence 9.4-25%.
+     Hypertension, alcohol, prior stroke main risk factors.
+"""
+
+import numpy as np
+import pandas as pd
+import argparse
+import os
+
+SCENARIOS = {
+    'tertiary_hospital': {
+        'description': 'Tertiary hospital with CT/MRI, stroke unit, '
+                       'physiotherapy, some thrombolysis capability '
+                       '(e.g., Muhimbili Tanzania, UCH Ibadan, KBTH Ghana)',
+        'ct_available': True,
+        'mri_available': True,
+        'thrombolysis_available': True,
+        'stroke_unit': True,
+        'physiotherapy': True,
+        'mortality_mod': 0.7,
+    },
+    'district_hospital': {
+        'description': 'District hospital, no CT, clinical diagnosis, '
+                       'basic BP management, no stroke unit '
+                       '(e.g., district hospitals Malawi, Uganda)',
+        'ct_available': False,
+        'mri_available': False,
+        'thrombolysis_available': False,
+        'stroke_unit': False,
+        'physiotherapy': False,
+        'mortality_mod': 1.0,
+    },
+    'rural_facility': {
+        'description': 'Rural facility, no imaging, delayed referral, '
+                       'minimal management '
+                       '(e.g., rural DRC, Chad, South Sudan)',
+        'ct_available': False,
+        'mri_available': False,
+        'thrombolysis_available': False,
+        'stroke_unit': False,
+        'physiotherapy': False,
+        'mortality_mod': 1.5,
+    },
+}
+
+
+def generate_dataset(n=10000, seed=42, scenario='district_hospital'):
+    rng = np.random.default_rng(seed)
+    sc = SCENARIOS[scenario]
+
+    records = []
+
+    for idx in range(n):
+        rec = {'id': idx + 1}
+
+        # ── 1. Demographics [3] ──
+        rec['age_years'] = max(18, min(95, int(rng.normal(59, 14))))
+        rec['sex'] = rng.choice(['M', 'F'], p=[0.52, 0.48])
+        rec['rural'] = 1 if rng.random() < 0.60 else 0
+
+        # ── 2. Risk factors [6][7] ──
+        rec['hypertension'] = 1 if rng.random() < 0.75 else 0
+        rec['bp_on_treatment'] = 0
+        if rec['hypertension']:
+            rec['bp_on_treatment'] = 1 if rng.random() < 0.30 else 0
+        rec['bp_controlled'] = 0
+        if rec['bp_on_treatment']:
+            rec['bp_controlled'] = 1 if rng.random() < 0.25 else 0
+
+        rec['systolic_bp'] = max(90, min(260, int(rng.normal(
+            160 if rec['hypertension'] and not rec['bp_controlled'] else 130, 25))))
+        rec['diastolic_bp'] = max(50, min(150, int(rng.normal(
+            95 if rec['hypertension'] and not rec['bp_controlled'] else 80, 15))))
+
+        rec['diabetes'] = 1 if rng.random() < 0.20 else 0
+        rec['atrial_fibrillation'] = 1 if rng.random() < 0.08 else 0
+        rec['smoking'] = 1 if rng.random() < 0.15 else 0
+        rec['alcohol_heavy'] = 1 if rng.random() < 0.20 else 0
+        rec['hiv_positive'] = 1 if rng.random() < 0.08 else 0
+        rec['bmi'] = round(max(15, min(45, rng.normal(26, 5))), 1)
+        rec['prior_stroke'] = 1 if rng.random() < 0.12 else 0
+        rec['prior_tia'] = 1 if rng.random() < 0.05 else 0
+        rec['sickle_cell'] = 1 if rng.random() < 0.03 else 0
+
+        # ── 3. Stroke characteristics [3] ──
+        rec['stroke_type'] = rng.choice(
+            ['ischaemic', 'intracerebral_haemorrhage', 'subarachnoid_haemorrhage'],
+            p=[0.60, 0.32, 0.08])
+
+        if rec['stroke_type'] == 'ischaemic':
+            rec['ischaemic_subtype'] = rng.choice(
+                ['large_vessel', 'small_vessel', 'cardioembolic', 'undetermined'],
+                p=[0.30, 0.25, 0.15, 0.30])
+        else:
+            rec['ischaemic_subtype'] = 'n/a'
+
+        rec['nihss_score'] = max(0, min(42, int(rng.exponential(8))))
+        rec['gcs_score'] = max(3, min(15, int(rng.normal(
+            12 if rec['nihss_score'] < 15 else 8, 3))))
+
+        rec['hemiparesis'] = 1 if rng.random() < 0.75 else 0
+        rec['aphasia'] = 1 if rng.random() < 0.30 else 0
+        rec['dysphagia'] = 1 if rng.random() < 0.35 else 0
+        rec['visual_field_defect'] = 1 if rng.random() < 0.15 else 0
+        rec['neglect'] = 1 if rng.random() < 0.10 else 0
+
+        rec['onset_to_arrival_hours'] = max(1, int(rng.exponential(
+            24 if scenario == 'rural_facility' else 12)))
+
+        # ── 4. Diagnosis ──
+        rec['ct_performed'] = 0
+        if sc['ct_available']:
+            rec['ct_performed'] = 1 if rng.random() < 0.80 else 0
+
+        rec['mri_performed'] = 0
+        if sc['mri_available'] and rec['ct_performed']:
+            rec['mri_performed'] = 1 if rng.random() < 0.20 else 0
+
+        rec['ecg_done'] = 1 if rng.random() < (0.70 if sc['stroke_unit'] else 0.30) else 0
+        rec['echo_done'] = 1 if rng.random() < (0.30 if sc['ct_available'] else 0.05) else 0
+        rec['clinical_diagnosis_only'] = 1 if not rec['ct_performed'] else 0
+
+        # ── 5. Treatment [5] ──
+        rec['thrombolysis'] = 0
+        if (sc['thrombolysis_available'] and rec['stroke_type'] == 'ischaemic'
+                and rec['onset_to_arrival_hours'] <= 4.5):
+            rec['thrombolysis'] = 1 if rng.random() < 0.10 else 0
+
+        rec['antiplatelet'] = 0
+        if rec['stroke_type'] == 'ischaemic':
+            rec['antiplatelet'] = 1 if rng.random() < (0.75 if sc['ct_available'] else 0.40) else 0
+
+        rec['anticoagulant'] = 0
+        if rec['atrial_fibrillation'] and rec['stroke_type'] == 'ischaemic':
+            rec['anticoagulant'] = 1 if rng.random() < 0.20 else 0
+
+        rec['antihypertensive_acute'] = 1 if rec['systolic_bp'] > 180 and rng.random() < 0.60 else 0
+        rec['statin'] = 1 if rng.random() < (0.40 if sc['stroke_unit'] else 0.10) else 0
+        rec['surgical_evacuation'] = 0
+        if rec['stroke_type'] == 'intracerebral_haemorrhage' and sc['ct_available']:
+            rec['surgical_evacuation'] = 1 if rng.random() < 0.05 else 0
+
+        rec['nasogastric_feeding'] = 0
+        if rec['dysphagia']:
+            rec['nasogastric_feeding'] = 1 if rng.random() < (0.50 if sc['stroke_unit'] else 0.15) else 0
+
+        rec['physiotherapy_started'] = 0
+        if sc['physiotherapy']:
+            rec['physiotherapy_started'] = 1 if rng.random() < 0.50 else 0
+
+        rec['dvt_prophylaxis'] = 1 if rng.random() < (0.40 if sc['stroke_unit'] else 0.05) else 0
+
+        # ── 6. Complications ──
+        rec['aspiration_pneumonia'] = 0
+        if rec['dysphagia'] and not rec['nasogastric_feeding']:
+            rec['aspiration_pneumonia'] = 1 if rng.random() < 0.25 else 0
+
+        rec['dvt_pe'] = 1 if rng.random() < (0.03 if rec['dvt_prophylaxis'] else 0.08) else 0
+        rec['pressure_ulcer'] = 1 if rng.random() < 0.10 else 0
+        rec['seizure'] = 1 if rng.random() < 0.08 else 0
+        rec['haemorrhagic_transformation'] = 0
+        if rec['stroke_type'] == 'ischaemic':
+            rec['haemorrhagic_transformation'] = 1 if rng.random() < 0.05 else 0
+
+        # ── 7. Outcome [2][4][5] ──
+        base_mort = 0.25
+        if rec['stroke_type'] == 'intracerebral_haemorrhage':
+            base_mort = 0.40
+        elif rec['stroke_type'] == 'subarachnoid_haemorrhage':
+            base_mort = 0.45
+
+        mort = base_mort * sc['mortality_mod']
+        if rec['gcs_score'] <= 8:
+            mort *= 1.8
+        if rec['nihss_score'] > 20:
+            mort *= 1.5
+        if rec['thrombolysis']:
+            mort *= 0.6
+        if rec['aspiration_pneumonia']:
+            mort *= 1.5
+        if rec['age_years'] > 75:
+            mort *= 1.3
+
+        rec['outcome_30_day'] = 'died' if rng.random() < min(mort, 0.80) else 'survived'
+
+        rec['mrs_discharge'] = 5
+        if rec['outcome_30_day'] == 'survived':
+            if rec['nihss_score'] < 5:
+                rec['mrs_discharge'] = rng.choice([0, 1, 2, 3], p=[0.10, 0.25, 0.35, 0.30])
+            elif rec['nihss_score'] < 15:
+                rec['mrs_discharge'] = rng.choice([2, 3, 4], p=[0.20, 0.45, 0.35])
+            else:
+                rec['mrs_discharge'] = rng.choice([3, 4, 5], p=[0.15, 0.45, 0.40])
+
+        rec['disability'] = 'none' if rec['mrs_discharge'] <= 1 else (
+            'mild' if rec['mrs_discharge'] == 2 else (
+            'moderate' if rec['mrs_discharge'] == 3 else 'severe'))
+
+        rec['hospital_days'] = max(1, min(60, int(rng.exponential(10))))
+        if rec['outcome_30_day'] == 'died':
+            rec['hospital_days'] = max(1, min(30, int(rng.exponential(5))))
+
+        records.append(rec)
+
+    df = pd.DataFrame(records)
+
+    print(f"\n{'='*65}")
+    print(f"Stroke — {scenario} (n={n}, seed={seed})")
+    print(f"{'='*65}")
+    print(f"\n  Ischaemic: {(df['stroke_type']=='ischaemic').mean()*100:.1f}%")
+    print(f"  Haemorrhagic: {(df['stroke_type']=='intracerebral_haemorrhage').mean()*100:.1f}%")
+    print(f"  Hypertension: {df['hypertension'].mean()*100:.1f}%")
+    print(f"  CT performed: {df['ct_performed'].mean()*100:.1f}%")
+    died = (df['outcome_30_day']=='died').mean()*100
+    print(f"  30-day mortality: {died:.1f}%")
+
+    return df
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description='Generate stroke dataset')
+    parser.add_argument('--scenario', type=str, default='district_hospital',
+                        choices=list(SCENARIOS.keys()))
+    parser.add_argument('--n', type=int, default=10000)
+    parser.add_argument('--seed', type=int, default=42)
+    parser.add_argument('--output', type=str, default=None)
+    parser.add_argument('--all-scenarios', action='store_true')
+    args = parser.parse_args()
+
+    os.makedirs('data', exist_ok=True)
+
+    if args.all_scenarios:
+        for sc_name in SCENARIOS:
+            df = generate_dataset(n=args.n, seed=args.seed, scenario=sc_name)
+            out = os.path.join('data', f'stroke_{sc_name}.csv')
+            df.to_csv(out, index=False)
+            print(f"  → Saved to {out}\n")
+    else:
+        df = generate_dataset(n=args.n, seed=args.seed, scenario=args.scenario)
+        out = args.output or os.path.join('data', f'stroke_{args.scenario}.csv')
+        df.to_csv(out, index=False)
+        print(f"  → Saved to {out}")

requirements.txt

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+numpy>=1.24
+pandas>=2.0
+matplotlib>=3.7

validate_dataset.py

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+#!/usr/bin/env python3
+"""Validation & Diagnostic Visualization for Stroke Dataset."""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+SCENARIOS = ['tertiary_hospital', 'district_hospital', 'rural_facility']
+
+
+def load_scenarios(data_dir='data'):
+    dfs = {}
+    for sc in SCENARIOS:
+        path = os.path.join(data_dir, f'stroke_{sc}.csv')
+        if os.path.exists(path):
+            dfs[sc] = pd.read_csv(path)
+    return dfs
+
+
+def make_report(dfs, output='validation_report.png'):
+    fig, axes = plt.subplots(4, 2, figsize=(16, 22))
+    fig.suptitle('Stroke & Cerebrovascular Disease — Validation Report',
+                 fontsize=16, fontweight='bold', y=0.98)
+    df = dfs.get('district_hospital', list(dfs.values())[0])
+    colors = ['#2ecc71', '#f39c12', '#e74c3c']
+
+    ax = axes[0, 0]
+    x = np.arange(len(SCENARIOS))
+    mort = [(dfs[sc]['outcome_30_day'] == 'died').mean() * 100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, mort, color=colors, alpha=0.8)
+    ax.set_xticks(x)
+    ax.set_xticklabels(['Tertiary', 'District', 'Rural'], fontsize=9)
+    for i, v in enumerate(mort):
+        ax.text(i, v + 0.5, f'{v:.1f}%', ha='center', fontsize=10)
+    ax.set_ylabel('30-Day Mortality (%)')
+    ax.set_title('30-Day Mortality (SSA pooled ~30%)')
+
+    ax = axes[0, 1]
+    types = ['ischaemic', 'intracerebral_haemorrhage', 'subarachnoid_haemorrhage']
+    t_labels = ['Ischaemic', 'ICH', 'SAH']
+    vals = [(df['stroke_type'] == t).mean() * 100 for t in types]
+    t_colors = ['#3498db', '#e74c3c', '#f39c12']
+    ax.pie(vals, labels=t_labels, autopct='%1.0f%%', colors=t_colors,
+           startangle=90, textprops={'fontsize': 10})
+    ax.set_title('Stroke Type (60% ischaemic, 32% ICH)')
+
+    ax = axes[1, 0]
+    risks = ['hypertension', 'diabetes', 'atrial_fibrillation', 'smoking',
+             'alcohol_heavy', 'prior_stroke', 'hiv_positive']
+    r_labels = ['HTN', 'DM', 'AF', 'Smoking', 'Alcohol', 'Prior Stroke', 'HIV']
+    vals = [df[r].mean() * 100 for r in risks]
+    ax.barh(range(7), vals, color='#e74c3c', alpha=0.7)
+    ax.set_yticks(range(7))
+    ax.set_yticklabels(r_labels, fontsize=8)
+    for i, v in enumerate(vals):
+        ax.text(v + 0.3, i, f'{v:.0f}%', va='center', fontsize=8)
+    ax.set_xlabel('Prevalence (%)')
+    ax.set_title('Risk Factors (HTN #1)')
+
+    ax = axes[1, 1]
+    for t, col, lab in [('ischaemic', '#3498db', 'Ischaemic'),
+                         ('intracerebral_haemorrhage', '#e74c3c', 'ICH')]:
+        sub = df[df['stroke_type'] == t]
+        m = (sub['outcome_30_day'] == 'died').mean() * 100 if len(sub) > 0 else 0
+        ax.bar(lab, m, color=col, alpha=0.8)
+        ax.text(lab, m + 0.5, f'{m:.0f}%', ha='center', fontsize=10)
+    ax.set_ylabel('30-Day Mortality (%)')
+    ax.set_title('Mortality by Stroke Type (ICH > Ischaemic)')
+
+    ax = axes[2, 0]
+    for sc_name, col in zip(SCENARIOS, colors):
+        if sc_name in dfs:
+            d = dfs[sc_name]
+            ax.hist(d['onset_to_arrival_hours'].clip(upper=72), bins=20,
+                    alpha=0.4, color=col,
+                    label=sc_name.replace('_', ' ').title()[:10], edgecolor='white')
+    ax.set_xlabel('Hours from Onset to Arrival')
+    ax.set_title('Presentation Delay')
+    ax.legend(fontsize=7)
+
+    ax = axes[2, 1]
+    cascade = ['CT Scan', 'Antiplatelet', 'Thrombolysis', 'Statin', 'Physio']
+    for i, sc_name in enumerate(SCENARIOS):
+        if sc_name in dfs:
+            d = dfs[sc_name]
+            vals = [d['ct_performed'].mean()*100, d['antiplatelet'].mean()*100,
+                    d['thrombolysis'].mean()*100, d['statin'].mean()*100,
+                    d['physiotherapy_started'].mean()*100]
+            ax.plot(range(5), vals, 'o-', label=sc_name.replace('_', ' ').title()[:10],
+                    color=colors[i], linewidth=2, markersize=6)
+    ax.set_xticks(range(5))
+    ax.set_xticklabels(cascade, fontsize=8)
+    ax.set_ylabel('Rate (%)')
+    ax.set_title('Treatment Cascade (thrombolysis near-zero SSA)')
+    ax.legend(fontsize=7)
+
+    ax = axes[3, 0]
+    survived = df[df['outcome_30_day'] == 'survived']
+    if len(survived) > 0:
+        mrs = survived['mrs_discharge'].value_counts().sort_index()
+        ax.bar(mrs.index.astype(str), mrs.values, color='#3498db', alpha=0.8)
+    ax.set_xlabel('Modified Rankin Scale')
+    ax.set_ylabel('Count')
+    ax.set_title('Disability at Discharge (mRS)')
+
+    ax = axes[3, 1]
+    died = df[df['outcome_30_day'] == 'died']['age_years']
+    surv = df[df['outcome_30_day'] == 'survived']['age_years']
+    ax.hist(surv, bins=20, alpha=0.5, color='#2ecc71', label='Survived', edgecolor='white')
+    ax.hist(died, bins=20, alpha=0.7, color='#e74c3c', label='Died', edgecolor='white')
+    ax.set_xlabel('Age (years)')
+    ax.set_title('Age Distribution (mean ~59, younger than HICs)')
+    ax.legend(fontsize=8)
+
+    plt.tight_layout(rect=[0, 0, 1, 0.97])
+    plt.savefig(output, dpi=150, bbox_inches='tight')
+    print(f'Saved validation report to {output}')
+    plt.close()
+
+
+if __name__ == '__main__':
+    dfs = load_scenarios()
+    if dfs:
+        make_report(dfs)