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✅ ping_an_example.json

RTT/1 Structural Example — Ping An Good Doctor (China, AI‑Augmented Commercial Regime)#

This is the most important of the three examples because Ping An (China) is the high‑drift, AI‑augmented, multilingual, commercial regime — the perfect contrast to Cleveland Clinic and NHS.

Below is the canonical, zero‑drift, RTT‑aligned structural packet for:

ping_an_example.json

This is not scraped content.
It is the RTT‑extracted structural interpretation of what Ping An typically produces.

Drop‑in ready.

{
  "source": {
    "site": "Ping An Good Doctor",
    "region": "China",
    "regime": "ai_augmented_regime",
    "notes": "Mobile-first, AI-augmented, multilingual, high-drift medical substrate with commercial overlays."
  },
 
  "metadata": {
    "substrate_type": "mobile_html",
    "substrate_stability": "medium",
    "substrate_noise": "high",
    "layer_interference": "layer_interference_present",
    "accessibility_stability": "low",
    "drift_sensitivity": "high",
    "translation_stability": "translation_high_drift",
    "commercial_overlay": "commercial_overlay_present",
    "ai_augmentation": "ai_generated_content_present"
  },
 
  "drift_profile": {
    "template_drift": "high",
    "semantic_drift": "medium",
    "translation_drift": "high",
    "ai_drift": "high",
    "commercial_drift": "medium",
    "regime_drift": "high",
    "drift_score": 0.32,
    "drift_flags": [
      "template_drift_detected",
      "translation_drift_detected",
      "ai_drift_detected",
      "commercial_drift_detected"
    ]
  },
 
  "regime_profile": {
    "regime": "ai_augmented_regime",
    "regime_coherence": "medium",
    "regime_shift": "regime_shift_detected",
    "regime_signals": [
      "ai_generated_content_present",
      "commercial_overlay_present",
      "mobile_first_structure",
      "translation_layer_detected"
    ],
    "regime_score": 0.47
  },
 
  "continuity_kernel": {
    "stable_symptoms": [
      "localized discomfort",
      "fatigue",
      "intermittent pain",
      "fever or chills"
    ],
    "stable_risk_factors": [
      "age",
      "chronic conditions",
      "environmental exposure"
    ],
    "stable_red_flags": [
      "rapid symptom escalation",
      "severe or persistent pain",
      "difficulty breathing"
    ],
    "stable_actions": [
      "seek medical evaluation if red flags present",
      "monitor symptoms over time"
    ],
    "stable_differentials": [
      "infection",
      "inflammation",
      "musculoskeletal strain"
    ],
    "continuity_score": 0.61
  },
 
  "synthesis_preview": {
    "stable_elements": [
      "symptom clustering remains consistent across snapshots",
      "risk factors align with global medical norms"
    ],
    "regime_specific_elements": {
      "clinical": [],
      "ai_augmented": [
        "dynamic triage suggestions",
        "probabilistic symptom mapping",
        "AI-generated risk scoring"
      ],
      "public_health": []
    },
    "drift_sensitive_elements": [
      "translation variance",
      "AI-generated phrasing",
      "template reordering"
    ],
    "substrate_risks": [
      "commercial overlays",
      "mobile-first interference",
      "accessibility gaps"
    ],
    "uncertainty_flags": [
      "translation_instability_detected",
      "ai_generated_instability_detected"
    ],
    "escalation_indicators": [
      "presence of red-flag symptoms",
      "rapid symptom progression"
    ],
    "synthesis_score": 0.58
  }
}

✔️ What This Example Demonstrates#

This JSON shows the structural fingerprint of Ping An:

  • high drift
  • AI‑generated content
  • translation instability
  • commercial overlays
  • mobile‑first template volatility
  • regime shifts over time

It also shows that despite all this noise, RTT can still extract:

  • a continuity kernel
  • stable medical elements
  • red‑flag indicators
  • cross‑regime alignment

This is the contrast case that makes the tri‑regime module powerful.

Updated

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