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Antigravity Agent 9148c358d0 docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거

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.

2026-07-05 00:33:48 +09:00

4.8 KiB

Raw Blame History

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title

Autism Spectrum Disorder

매 한 줄

"매 social communication deficits + restricted/repetitive behaviors 의 neurodevelopmental condition — 매 dimensional spectrum." 매 DSM-5 (2013) 의 single diagnosis (Asperger, PDD-NOS 의 merge) — 매 prevalence ~1/36 (CDC 2023) — 매 2026 에 polygenic + multimodal AI biomarkers, early intervention 의 efficacy 의 evidence-based.

매 핵심

매 DSM-5 criteria

A. Social communication: 매 social-emotional reciprocity, nonverbal communication, relationships 의 deficits (all 3).
B. Restricted/repetitive: 매 stereotyped behavior, insistence on sameness, restricted interests, sensory atypicality (≥2 of 4).
C. Early developmental period (may not manifest until demands exceed capacity).
D. Functional impairment.
E. Not better explained by ID/global delay.

매 levels of support

Level 1: requiring support.
Level 2: requiring substantial support.
Level 3: requiring very substantial support.

매 응용

Early screening (M-CHAT, ADOS-2).
Multimodal AI diagnosis (eye-tracking + voice + behavioral).
Personalized intervention (ABA, ESDM, JASPER).

💻 패턴

M-CHAT-R/F scoring

def mchat_rf_score(responses):
    # 20 items, certain answers indicate risk
    risk_answers = {
        1: "no", 2: "no", 3: "no", 4: "no", 5: "no",
        6: "no", 7: "no", 8: "no", 9: "no", 10: "no",
        11: "yes", 12: "yes", 13: "no", 14: "no", 15: "no",
        16: "no", 17: "no", 18: "yes", 19: "no", 20: "yes",
    }
    score = sum(1 for k, v in responses.items() if v == risk_answers[k])
    if score >= 8: return "high"
    if score >= 3: return "medium"  # follow-up interview
    return "low"

import numpy as np

def social_attention_ratio(gaze_xy, face_aoi, object_aoi):
    in_face = points_in_aoi(gaze_xy, face_aoi).sum()
    in_obj = points_in_aoi(gaze_xy, object_aoi).sum()
    # Lower ratio observed in ASD vs TD
    return in_face / (in_face + in_obj + 1e-8)

Repetitive behavior detection (accelerometer)

from scipy.signal import find_peaks, welch

def stereotypy_score(accel_xyz, fs=50):
    mag = np.linalg.norm(accel_xyz, axis=1)
    f, psd = welch(mag, fs=fs, nperseg=512)
    # Stereotypies show narrow-band power 1-5 Hz
    band_power = psd[(f >= 1) & (f <= 5)].sum()
    total = psd.sum()
    return band_power / total

Voice prosody features

import librosa

def prosody_features(wav, sr):
    f0 = librosa.yin(wav, fmin=80, fmax=400, sr=sr)
    f0 = f0[f0 > 0]
    return {
        "f0_mean": np.mean(f0),
        "f0_std": np.std(f0),     # often atypical (mono- or sing-song)
        "f0_range": np.ptp(f0),
        "speaking_rate": estimate_rate(wav, sr),
    }

Polygenic risk score

def prs(genotype, weights):
    # weights: dict snp_id -> beta from GWAS
    score = 0.0
    for snp, dose in genotype.items():
        if snp in weights:
            score += dose * weights[snp]
    return score

매 결정 기준

상황	Approach
Toddler screen	M-CHAT-R/F at 18 + 24 months
Diagnostic confirm	ADOS-2 + ADI-R (gold standard)
Early intervention (<3y)	ESDM (Early Start Denver Model)
School-age	ABA, social skills groups, IEP
Co-occurring anxiety	CBT (modified), SSRI
Aggression / SIB	FBA + behavioral plan; meds last

기본값: 매 early screen → multidisciplinary eval → individualized plan.

🔗 Graph

🤖 LLM 활용

언제: 매 caregiver psychoeducation, 매 IEP draft, 매 social-story generation. 언제 X: 매 diagnosis, 매 medication — 매 clinician 의.

❌ 안티패턴

Single-snapshot diagnosis: 매 longitudinal observation 의 needed.
One-size-fits-all therapy: 매 high heterogeneity — 매 individualization.
MMR vaccine link: 매 debunked (Wakefield retracted 2010).
Cure-focused framing: 매 neurodiversity perspective 의 respect.

🧪 검증 / 중복

Verified (DSM-5-TR 2022, CDC ADDM 2023, Lord et al. Lancet 2018).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — DSM criteria + screening/biomarker patterns

4.8 KiB Raw Blame History