refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-fda-clearance-medical-device-app
+title: FDA Clearance (Medical Device Approval)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [510k, fda-510k, premarket-notification]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [regulatory, medical-device, fda, compliance]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: english
+  framework: regulatory
+---
+
+# FDA Clearance (Medical Device Approval)
+
+## 매 한 줄
+> **"매 device 가 predicate 에 substantially equivalent 인가의 증명"**. FDA 의 medical device 시장 진입 경로 — 매 510(k) clearance / De Novo / PMA 의 3 trail. 매 software-as-medical-device (SaMD) 와 AI/ML 의 부상으로 2026 현재 적응형 review pathway 의 도입.
+
+## 매 핵심
+
+### 매 Class
+- **Class I** (low risk): 매 general controls. 대부분 exempt.
+- **Class II** (moderate): 매 510(k) submission 필요.
+- **Class III** (high risk, life-supporting): 매 PMA — full clinical trial.
+
+### 매 경로
+- **510(k)**: predicate device 와 의 substantial equivalence — 매 fastest (3-6 months).
+- **De Novo**: novel low/moderate risk — predicate 의 부재 시.
+- **PMA** (Premarket Approval): Class III — 매 most rigorous, 1-3 year.
+- **Breakthrough Designation**: priority review for unmet need.
+
+### 매 응용
+1. AI 의료기기 — IDx-DR (diabetic retinopathy), Aidoc (radiology triage).
+2. Surgical robot — da Vinci, Intuitive.
+3. Continuous glucose monitor — Dexcom G7.
+4. SaMD — Apple Watch ECG (De Novo), Cardiologs.
+
+## 💻 패턴
+
+### Predicate Search
+```python
+import requests
+
+def search_510k(device_name: str, limit: int = 50):
+    """openFDA 의 510k database 의 predicate 검색."""
+    url = "https://api.fda.gov/device/510k.json"
+    params = {"search": f'device_name:"{device_name}"', "limit": limit}
+    r = requests.get(url, params=params, timeout=30)
+    r.raise_for_status()
+    return r.json().get("results", [])
+```
+
+### Substantial Equivalence Comparison
+```python
+def compare_devices(subject: dict, predicate: dict):
+    """매 indications / technology / performance 의 비교 표 의 생성."""
+    rows = []
+    for field in ["indications_for_use", "technological_characteristics", "performance"]:
+        rows.append({
+            "field": field,
+            "subject": subject.get(field),
+            "predicate": predicate.get(field),
+            "different": subject.get(field) != predicate.get(field),
+        })
+    return rows
+```
+
+### Adverse Event Lookup (MAUDE)
+```python
+def maude_events(device_name: str, since: str = "2024-01-01"):
+    url = "https://api.fda.gov/device/event.json"
+    params = {
+        "search": f'device.generic_name:"{device_name}" AND date_received:[{since} TO now]',
+        "limit": 100,
+    }
+    return requests.get(url, params=params).json().get("results", [])
+```
+
+### SaMD Risk Categorization (IMDRF)
+```python
+def samd_category(intended_use: str, healthcare_situation: str) -> str:
+    """IMDRF SaMD: I-IV — 매 information vs treat/diagnose × non-serious/serious/critical."""
+    matrix = {
+        ("inform", "non-serious"): "I",
+        ("inform", "serious"): "II",
+        ("inform", "critical"): "II",
+        ("drive", "non-serious"): "II",
+        ("drive", "serious"): "III",
+        ("drive", "critical"): "III",
+        ("treat-diagnose", "non-serious"): "II",
+        ("treat-diagnose", "serious"): "III",
+        ("treat-diagnose", "critical"): "IV",
+    }
+    return matrix.get((intended_use, healthcare_situation), "unknown")
+```
+
+### PCCP (Predetermined Change Control Plan) for AI
+```yaml
+pccp:
+  modifications:
+    - type: retraining
+      trigger: quarterly with new data
+      validation: hold-out test set AUC > 0.9
+    - type: input expansion
+      trigger: new sensor model
+      validation: equivalence study
+  monitoring:
+    metrics: [sensitivity, specificity, demographic parity]
+    threshold: 5% degradation
+    action: rollback + FDA notification
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Predicate 존재 | 510(k) |
+| Novel low-risk | De Novo |
+| Life-supporting | PMA |
+| AI software | 510(k) + PCCP |
+| Unmet medical need | Breakthrough |
+
+**기본값**: predicate search 후 510(k) — 매 most devices 의 default.
+
+## 🔗 Graph
+- 변형: [[510k]]
+
+## 🤖 LLM 활용
+**언제**: predicate search / SE comparison drafting / adverse event summary.
+**언제 X**: 매 final regulatory submission — 매 RA professional review 의 필수.
+
+## ❌ 안티패턴
+- **Predicate cherry-picking**: 매 weakest predicate 의 선택 — FDA 의 reject.
+- **Algorithm change without PCCP**: 매 retrain 후 silent deploy — adulteration.
+- **510(k) for novel device**: 매 De Novo 가 필요한 경우 의 wrong path.
+
+## 🧪 검증 / 중복
+- Verified (FDA CDRH guidance, 21 CFR 807, IMDRF SaMD framework).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — FDA pathways + SaMD/PCCP 패턴 |