Antigravity Agent
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149 lines
4.8 KiB
Markdown
149 lines
4.8 KiB
Markdown
---
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id: wiki-2026-0508-autism-spectrum-disorder
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title: Autism Spectrum Disorder
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [ASD, Autism, 자폐스펙트럼장애]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [neurodevelopment, psychiatry, autism, dsm5]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: pandas/scikit-learn
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---
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# Autism Spectrum Disorder
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 DSM-5 criteria
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- **A. Social communication**: 매 social-emotional reciprocity, nonverbal communication, relationships 의 deficits (all 3).
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- **B. Restricted/repetitive**: 매 stereotyped behavior, insistence on sameness, restricted interests, sensory atypicality (≥2 of 4).
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- **C. Early developmental period** (may not manifest until demands exceed capacity).
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- **D. Functional impairment**.
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- **E. Not better explained by ID/global delay**.
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### 매 levels of support
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- **Level 1**: requiring support.
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- **Level 2**: requiring substantial support.
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- **Level 3**: requiring very substantial support.
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### 매 응용
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1. Early screening (M-CHAT, ADOS-2).
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2. Multimodal AI diagnosis (eye-tracking + voice + behavioral).
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3. Personalized intervention (ABA, ESDM, JASPER).
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## 💻 패턴
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### M-CHAT-R/F scoring
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```python
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def mchat_rf_score(responses):
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# 20 items, certain answers indicate risk
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risk_answers = {
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1: "no", 2: "no", 3: "no", 4: "no", 5: "no",
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6: "no", 7: "no", 8: "no", 9: "no", 10: "no",
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11: "yes", 12: "yes", 13: "no", 14: "no", 15: "no",
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16: "no", 17: "no", 18: "yes", 19: "no", 20: "yes",
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}
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score = sum(1 for k, v in responses.items() if v == risk_answers[k])
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if score >= 8: return "high"
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if score >= 3: return "medium" # follow-up interview
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return "low"
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```
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### Eye-tracking social attention
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```python
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import numpy as np
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def social_attention_ratio(gaze_xy, face_aoi, object_aoi):
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in_face = points_in_aoi(gaze_xy, face_aoi).sum()
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in_obj = points_in_aoi(gaze_xy, object_aoi).sum()
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# Lower ratio observed in ASD vs TD
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return in_face / (in_face + in_obj + 1e-8)
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```
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### Repetitive behavior detection (accelerometer)
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```python
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from scipy.signal import find_peaks, welch
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def stereotypy_score(accel_xyz, fs=50):
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mag = np.linalg.norm(accel_xyz, axis=1)
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f, psd = welch(mag, fs=fs, nperseg=512)
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# Stereotypies show narrow-band power 1-5 Hz
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band_power = psd[(f >= 1) & (f <= 5)].sum()
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total = psd.sum()
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return band_power / total
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```
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### Voice prosody features
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```python
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import librosa
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def prosody_features(wav, sr):
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f0 = librosa.yin(wav, fmin=80, fmax=400, sr=sr)
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f0 = f0[f0 > 0]
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return {
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"f0_mean": np.mean(f0),
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"f0_std": np.std(f0), # often atypical (mono- or sing-song)
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"f0_range": np.ptp(f0),
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"speaking_rate": estimate_rate(wav, sr),
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}
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```
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### Polygenic risk score
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```python
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def prs(genotype, weights):
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# weights: dict snp_id -> beta from GWAS
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score = 0.0
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for snp, dose in genotype.items():
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if snp in weights:
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score += dose * weights[snp]
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return score
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Toddler screen | M-CHAT-R/F at 18 + 24 months |
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| Diagnostic confirm | ADOS-2 + ADI-R (gold standard) |
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| Early intervention (<3y) | ESDM (Early Start Denver Model) |
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| School-age | ABA, social skills groups, IEP |
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| Co-occurring anxiety | CBT (modified), SSRI |
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| Aggression / SIB | FBA + behavioral plan; meds last |
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**기본값**: 매 early screen → multidisciplinary eval → individualized plan.
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## 🔗 Graph
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- 부모: [[Neurodevelopmental Disorders]]
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- 응용: [[ABA]]
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- Adjacent: [[ADHD]] · [[Amygdala Hyperactivity]]
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## 🤖 LLM 활용
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**언제**: 매 caregiver psychoeducation, 매 IEP draft, 매 social-story generation.
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**언제 X**: 매 diagnosis, 매 medication — 매 clinician 의.
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## ❌ 안티패턴
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- **Single-snapshot diagnosis**: 매 longitudinal observation 의 needed.
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- **One-size-fits-all therapy**: 매 high heterogeneity — 매 individualization.
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- **MMR vaccine link**: 매 debunked (Wakefield retracted 2010).
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- **Cure-focused framing**: 매 neurodiversity perspective 의 respect.
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## 🧪 검증 / 중복
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- Verified (DSM-5-TR 2022, CDC ADDM 2023, Lord et al. Lancet 2018).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — DSM criteria + screening/biomarker patterns |
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