[G1-Sync] Manual knowledge update

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id: wiki-2026-0508-geriatric-medicine
title: Geriatric Medicine
category: 10_Wiki/Topics
status: needs_review
status: verified
canonical_id: self
aliases: [P-Reinforce-AUTO-GERE-001]
aliases: [geriatrics, aging, elderly care, frailty, polypharmacy, CGA]
duplicate_of: none
source_trust_level: A
confidence_score: 0.93
tags: [auto-reinforced, geriatric-medicine, aging, healthcare, frailty, chronic-disease, _system-medicine]
confidence_score: 0.85
verification_status: applied
tags: [medicine, geriatrics, aging, frailty, healthcare, ai-medicine]
raw_sources: []
last_reinforced: 2026-04-20
last_reinforced: 2026-05-10
github_commit: pending
inferred_by: Claude Opus 4.7 (auto-normalize 2026-05-08)
tech_stack:
language: Medical / Clinical
applicable_to: [Healthcare AI, Elderly Care, Risk Stratification]
---
# [[Geriatric-Medicine|Geriatric-Medicine]]
# Geriatric Medicine
## 📌 한 줄 통찰 (The Karpathy Summary)
> "복합적 약점의 관리: 단순히 병 하나를 고치는 것이 아니라, 노화로 인해 신체 전반의 회복력이 떨어진(Frailty) 노인 환자의 다중 질환과 인지 기능, 사회적 환경까지 통합적으로 보살피는 시스템적 의료."
## 한 줄
> **"매 elderly (65+) 의 specific medical care"**. 매 frailty + multimorbidity + polypharmacy + cognitive decline + functional decline. 매 modern: 매 ML risk stratification + 매 fall detection + 매 dementia screening.
## 📖 구조화된 지식 (Synthesized Content)
노인 의학(Geriatric-Medicine)은 고령자의 질병 치료뿐만 아니라 기능 유지 및 삶의 질 향상을 목적으로 하는 의학의 한 분야입니다.
## 매 핵심
1. **Geriatric Syndrome (노인성 증후군)**:
* 단일 질환보다는 섬망, 낙상, 실금, 욕창, 노쇠(Frailty) 등 여러 원인이 얽힌 증후군 중심 접근.
* **Polypharmacy (다제복용)**: 여러 약의 상호작용으로 인한 부작용 관리 필수. ([[Enzyme-Inhibition-Kinetics|Enzyme-Inhibition-Kinetics]]와 연결)
2. **왜 중요한가?**:
* 초고령 사회 진입에 따라 사회적 비용 정책과 정밀 의료 정책의 핵심 전장이 되고 있기 때문임. ([[Sustainability|Sustainability]]와 연결)
### 매 syndromes (Geriatric Giants)
- **Frailty**.
- **Falls**.
- **Cognitive decline / dementia**.
- **Incontinence**.
- **Iatrogenic** (medication-related).
## ⚠️ 모순 및 업데이트 (Contradictions & Updates)
- **과거 데이터와의 충돌**: 과거에는 노화를 '필연적 쇠퇴 정책'으로만 보았으나, 현대 정책은 적극적인 개입 정책(재활, 운동, 영양)을 통해 '건강 수명 정책'을 연장할 수 있다는 활기찬 노년 정책(Active aging)을 지향함(RL Update).
- **정책 변화(RL Update)**: 이제는 단순 환자 진료 정책을 넘어, 웨어러블 센서 정책과 AI 가 가정 내 낙상 위험 정책을 실시간 감지하여 미리 방지하는 '항시적 모니터링 체계'로 진화 중임. ([[Etiology-of-Disease|Etiology-of-Disease]]와 연결)
### 매 framework
- **CGA** (Comprehensive Geriatric Assessment): 매 medical + functional + psychological + social.
- **Frailty index** (Rockwood).
- **ADL / IADL**: 매 activities of daily living.
- **MMSE / MoCA**: 매 cognitive screen.
## 🔗 지식 연결 (Graph)
- [[Enzyme-Inhibition-Kinetics|Enzyme-Inhibition-Kinetics]], [[Sustainability|Sustainability]], [[Etiology-of-Disease|Etiology-of-Disease]], Bio-Informatics, [[Scientific-Method|Scientific-Method]], [[Reliability|Reliability]]
- **Key Focus**: Comprehensive Geriatric [[Assessment|Assessment]] (CGA).
---
### 매 modern AI
- **Risk stratification**: 매 readmission, fall, mortality.
- **Wearable monitoring**: 매 fall detection.
- **Dementia screening**: 매 voice / writing.
- **Polypharmacy**: 매 drug interaction LLM.
- **Telehealth**.
## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
### 매 응용
1. **Hospital readmission predict**.
2. **Fall risk score**.
3. **Frailty progression**.
4. **Medication review**.
5. **Cognitive assessment**.
6. **End-of-life planning**.
**언제 이 지식을 쓰는가:**
- *(TODO)*
## 💻 패턴
**언제 쓰면 안 되는가:**
- *(TODO)*
### Frailty index
```python
def frailty_index(deficits, max_deficits=70):
"""매 Rockwood frailty: count of deficits / total."""
n_deficit = sum(1 for d in deficits if d.present)
return n_deficit / max_deficits # 매 > 0.25 = frail
```
## 🧪 검증 상태 (Validation)
### Charlson Comorbidity Index
```python
CCI_WEIGHTS = {
'mi': 1, 'chf': 1, 'pvd': 1, 'cvd': 1, 'dementia': 1,
'copd': 1, 'connective': 1, 'ulcer': 1, 'liver_mild': 1,
'diabetes': 1, 'hemiplegia': 2, 'renal_mod_severe': 2,
'diabetes_complications': 2, 'tumor': 2, 'leukemia': 2,
'lymphoma': 2, 'liver_mod_severe': 3, 'metastatic_tumor': 6, 'aids': 6,
}
- **정보 상태:** needs_review
- **출처 신뢰도:** A
- **검토 이유:** *(P-Reinforce Phase 1 자동 정규화. 본문 검증 필요.)*
def cci_score(conditions, age):
score = sum(CCI_WEIGHTS.get(c, 0) for c in conditions)
if age >= 50: score += (age - 40) // 10
return score
```
## 🧬 중복 검사 (Duplicate Check)
### Fall risk (Morse Fall Scale)
```python
def morse_fall_scale(history_falls, secondary_diagnosis, ambulatory_aid, iv_therapy, gait, mental_status):
score = 0
if history_falls: score += 25
if secondary_diagnosis: score += 15
score += {'none': 0, 'crutch_cane_walker': 15, 'furniture': 30}[ambulatory_aid]
if iv_therapy: score += 20
score += {'normal': 0, 'weak': 10, 'impaired': 20}[gait]
score += {'oriented': 0, 'forgets_limit': 15}[mental_status]
risk = 'low' if score < 25 else 'medium' if score < 45 else 'high'
return score, risk
```
- **기존 유사 문서:** *(TODO: 인덱서 클러스터 리포트 참조)*
- **처리 방식:** UPDATE (자동 정규화)
- **처리 이유:** Phase 1 정규화 — 옛 템플릿/누락 필드 보강.
### Polypharmacy detection (Beers / STOPP)
```python
BEERS_INAPPROPRIATE = {'diphenhydramine': 'anticholinergic load', 'amitriptyline': 'TCA elderly', ...}
## 🕓 변경 이력 (Changelog)
def beers_check(medications):
flagged = []
for med in medications:
if med.name in BEERS_INAPPROPRIATE:
flagged.append({'med': med.name, 'reason': BEERS_INAPPROPRIATE[med.name]})
return flagged
```
| 날짜 | 변경 내용 | 처리 방식 | 신뢰도 |
|------|-----------|-----------|--------|
| 2026-05-08 | P-Reinforce Phase 1 정규화 (frontmatter + 헤더 표준화) | UPDATE | A |
### Drug interaction (LLM-aided)
```python
def drug_interaction_check(medications, llm):
prompt = f"""Check drug interactions for elderly patient.
Medications: {medications}
Output JSON list:
- pair: [drug1, drug2]
- severity: minor / moderate / severe
- mechanism
- recommendation"""
return json.loads(llm.generate(prompt))
```
### Cognitive screen (MoCA)
```python
def moca_total(scores):
"""매 30-point — 26+ normal, 18-25 mild, < 18 mod-severe."""
return sum(scores.values()) # 매 visuospatial + naming + memory + attention + language + abstraction + delayed recall + orientation
```
### Readmission risk (LACE)
```python
def lace_index(length_of_stay, acuity_admission, charlson, ed_visits_6mo):
"""매 30-day readmission risk."""
los_score = {1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 5, 7: 6, 8: 6, 9: 7}.get(min(length_of_stay, 9), 7)
acuity = 3 if acuity_admission else 0
charlson_score = min(charlson, 5)
ed_score = min(ed_visits_6mo, 4)
return los_score + acuity + charlson_score + ed_score
```
### Fall detection (wearable)
```python
def detect_fall(accelerometer_data, threshold_g=2.5):
"""매 spike + post-impact stillness."""
magnitudes = np.linalg.norm(accelerometer_data, axis=1)
spikes = np.where(magnitudes > threshold_g)[0]
for spike in spikes:
if spike + 50 < len(magnitudes):
post = magnitudes[spike+10:spike+50]
if post.std() < 0.1: # 매 still
return {'fall_detected': True, 'time': spike}
return {'fall_detected': False}
```
### Sarcopenia (SARC-F)
```python
def sarc_f_questionnaire():
return {
'strength': 'how much difficulty lifting 10lb',
'walking': 'how much difficulty walking across room',
'rising': 'how much difficulty rising from chair',
'climbing': 'how much difficulty climbing 10 stairs',
'falls': 'how many falls in last year',
}
# 매 score >= 4 → sarcopenia screen positive
```
### CGA (comprehensive)
```python
def comprehensive_geriatric_assessment(patient):
return {
'medical': cci_score(patient.conditions, patient.age),
'functional': adl_score(patient.adls),
'cognitive': moca_total(patient.moca),
'mood': geriatric_depression_scale(patient.gds),
'social': social_isolation_score(patient),
'frailty': frailty_index(patient.deficits),
'medications': beers_check(patient.medications),
}
```
### LLM clinical assistant
```python
def geriatric_consult(patient, llm):
prompt = f"""You are a geriatrics expert. For this patient:
{patient}
Output:
1. Top 3 medical priorities
2. Medication review (Beers / STOPP)
3. Functional intervention recommendations
4. Goals of care discussion points
DO NOT diagnose without confirmation. Always defer to attending."""
return llm.generate(prompt)
```
### End-of-life (POLST)
```python
@dataclass
class POLST:
cpr_preference: str # 매 attempt / DNR
medical_intervention: str # 매 full / selective / comfort
artificial_nutrition: str
def is_complete(self):
return all([self.cpr_preference, self.medical_intervention, self.artificial_nutrition])
```
## 매 결정 기준
| 상황 | Approach |
|---|---|
| Hospital admission | CGA + LACE |
| Fall risk | Morse + ambient sensor |
| Multi-medication | Beers / STOPP + LLM check |
| Cognitive concern | MoCA + DEM screen |
| Frailty | Rockwood index |
| End-of-life | POLST + family meeting |
**기본값**: 매 CGA + 매 risk stratification + 매 multi-disciplinary team + 매 wearable monitoring + 매 LLM medication check.
## 🔗 Graph
- 부모: [[Medicine]] · [[Aging]]
- 변형: [[Frailty]] · [[Dementia]] · [[Polypharmacy]]
- 응용: [[Risk-Stratification]] · [[Wearable-Health]]
- Adjacent: [[Healthcare-AI]] · [[End-of-Life-Care]]
## 🤖 LLM 활용
**언제**: 매 risk stratification. 매 medication review. 매 documentation.
**언제 X**: 매 final diagnosis (clinician-only).
## ❌ 안티패턴
- **Generic adult protocol**: 매 elderly different.
- **Polypharmacy ignore**: 매 cascade.
- **No functional assessment**: 매 hospitalization missing.
- **AI without clinician**: 매 liability.
## 🧪 검증 / 중복
- Verified (Beers Criteria 2023, AGS, MoCA, Rockwood frailty).
- 신뢰도 A.
## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-04-26 | Auto |
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — geriatric giants + 매 Frailty / Beers / Morse / MoCA / fall code |