9148c358d0
Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
4.6 KiB
4.6 KiB
id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack
| id | title | category | status | canonical_id | aliases | duplicate_of | source_trust_level | confidence_score | verification_status | tags | raw_sources | last_reinforced | github_commit | tech_stack | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| wiki-2026-0508-acl-prevention | ACL Prevention | 10_Wiki/Topics | verified | self |
|
none | A | 0.9 | applied |
|
2026-05-10 | pending |
|
ACL Prevention
매 한 줄
"매 ACL 부상 prevention 의 핵심 = neuromuscular training + landing mechanics + proprioception.". ACL (Anterior Cruciate Ligament) tear 의 70% 는 non-contact pivoting/landing 상황에서 발생하며, FIFA 11+, PEP, KIPP 같은 evidence-based program 이 incidence 를 50-70% 감소시킨다.
매 핵심
매 Risk Factor
- Modifiable: knee valgus on landing, weak hip abductors, quad-dominant deceleration, fatigue.
- Non-modifiable: female sex (2-8x risk), narrow intercondylar notch, generalized joint laxity.
- Environmental: cleat-surface interaction, fatigue late in match, prior injury history.
매 Prevention Pillar
- Neuromuscular training — plyometric + balance + strength, 2-3x/week.
- Landing mechanics — soft landing, knee over toe, hip-dominant.
- Core/hip strength — gluteus medius, hip external rotators.
- Proprioception — single-leg balance, perturbation training.
매 응용
- Youth soccer FIFA 11+ warmup (15 min pre-training).
- Female collegiate athletes PEP program.
- Post-ACLR return-to-sport batteries.
💻 패턴
Risk Score Aggregator
import pandas as pd
def acl_risk_score(athlete: dict) -> float:
"""0-1 risk; >0.6 → enroll in prevention program."""
score = 0.0
if athlete["sex"] == "F": score += 0.25
if athlete["prior_acl"]: score += 0.30
if athlete["knee_valgus_deg"] > 8: score += 0.20
if athlete["hop_lsi"] < 0.85: score += 0.15 # limb symmetry
if athlete["age"] < 18: score += 0.10
return min(score, 1.0)
Drop Vertical Jump (DVJ) Analyzer
import numpy as np
def knee_abduction_moment(forces, lever_arms):
"""Hewett 2005 — KAM > 25.3 Nm predicts ACL injury."""
return np.dot(forces, lever_arms)
def classify_landing(kam_nm: float) -> str:
if kam_nm > 25.3: return "high-risk"
if kam_nm > 15.0: return "moderate"
return "low-risk"
FIFA 11+ Session Builder
FIFA_11_PLUS = {
"part1_running": ["straight ahead", "hip out", "hip in", "circling partner"],
"part2_strength": ["bench", "sideways bench", "hamstrings", "single-leg stance"],
"part3_running": ["across pitch", "bounding", "plant-and-cut"],
}
def build_session(level: int = 1) -> list[str]:
drills = []
for part, items in FIFA_11_PLUS.items():
drills.extend(items if level >= 2 else items[:2])
return drills
Hop Test Battery
def hop_lsi(injured: float, uninjured: float) -> float:
"""Limb Symmetry Index — RTS threshold ≥ 0.90."""
return injured / uninjured
def cleared_for_rts(single_hop, triple_hop, crossover) -> bool:
return all(lsi >= 0.90 for lsi in (single_hop, triple_hop, crossover))
Cohort Tracking with Pandas
import pandas as pd
def season_incidence(df: pd.DataFrame) -> pd.Series:
"""ACL injuries per 1000 athlete-exposures."""
return df.groupby("team")["acl_injury"].sum() / df.groupby("team")["ae"].sum() * 1000
Fatigue Monitor
def fatigue_flag(rpe: int, srpe_load: int, acwr: float) -> bool:
"""Acute:chronic workload ratio > 1.5 → injury risk spike."""
return rpe >= 8 or acwr > 1.5
매 결정 기준
| 상황 | Approach |
|---|---|
| Youth team, no history | FIFA 11+ |
| Female collegiate | PEP / KIPP |
| Post-ACLR | Criterion-based RTS battery |
| Pro athlete in-season | Modified neuromuscular maintenance |
기본값: FIFA 11+ 2-3x/week.
🔗 Graph
🤖 LLM 활용
언제: structured risk-stratification, program selection, periodization advice. 언제 X: clinical diagnosis, surgical decision, individualized rehab prescription.
❌ 안티패턴
- Static stretching only: 매 효과 없음. Dynamic warmup 필요.
- Knee-only focus: hip/core ignore 시 valgus 재발.
- Volume without quality: poor landing form 의 reps 는 risk 증가.
- Generic program: sex/age/sport-specific tailoring 없으면 effect size 감소.
🧪 검증 / 중복
- Verified (Hewett 2005, Sadoghi 2012 meta-analysis, FIFA 11+ RCT).
- 신뢰도 A.
🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — full content with risk scoring + FIFA 11+ patterns |