[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_26":27,"_28":29,"_30":12,"_31":12,"_32":12,"_33":12,"_34":12,"_35":12,"_36":37},"routes/canvas.shared.$sharedTextdocId",{"_9":10,"_11":12},"sharedTextdoc",{"_13":14,"_15":-7,"_16":17,"_18":19,"_20":21,"_22":23,"_24":12,"_25":12},"isAuthenticated",false,"sharedTextdocId","68bdd7f4c2608191b3cfbdec88d4b63a","versionInt","title","Crash Force & M Tbi Risk Estimator (prototype)","type","code/react","content","import React, { useMemo, useState } from \"react\";\nimport { Card, CardContent } from \"@/components/ui/card\";\nimport { Button } from \"@/components/ui/button\";\nimport { AlertCircle, Info, Calculator } from \"lucide-react\";\n\n// ===================== NeuroGlympse Skin + Logo + Patient Factors =====================\n// Heuristic educational tool. Adds patient age, sex, and genetic predisposition as risk modifiers.\n\nconst g0 = 9.80665; // m/s^2\n\nfunction LogoNG(props: React.SVGProps) {\n return (\n \n );\n}\n\nfunction feetToMeters(ft: number) { return ft * 0.3048; }\nfunction lbfToNewtons(lbf: number) { return lbf * 4.4482216153; }\nfunction newtonsToLbf(N: number) { return N * 0.2248089431; }\nfunction round(v: number | undefined, d = 2) { if (v === undefined || Number.isNaN(v)) return undefined; const p = 10 ** d; return Math.round(v * p) / p; }\n\n// --- Core physics ---\nfunction gFromDistance(dv_mph: number, s_ft: number | undefined) {\n if (!dv_mph || !s_ft || s_ft <= 0) return undefined; return 0.0335 * (dv_mph * dv_mph) / s_ft; // mph & ft form\n}\nfunction gFromTime(dv_mph: number, dt_s: number | undefined) {\n if (!dv_mph || !dt_s || dt_s <= 0) return undefined; return 0.0456 * (dv_mph / dt_s); // mph & s form\n}\nfunction forceFromG(weight_lbf: number | undefined, g_avg: number | undefined) {\n if (!weight_lbf || !g_avg) return { lbf: undefined, N: undefined }; const lbf = weight_lbf * g_avg; const N = lbfToNewtons(lbf); return { lbf, N };\n}\n\n// --- Risk grading (heuristic proxy for mTBI risk) ---\n// Now includes patient age, sex, and genetic predisposition modifiers.\nfunction riskGrade({ dv_mph, restrained, intrusion, dt_s, headG, HIC, BrIC, age, sex, genetic }:{\n dv_mph: number; restrained: string; intrusion: boolean; dt_s?: number; headG?: number; HIC?: number; BrIC?: number;\n age?: number; sex?: 'male'|'female'|'other'|'unknown'; genetic?: 'none'|'moderate'|'high';\n}) {\n let level = 0; // 0 VL, 1 L, 2 LM, 3 M, 4 H, 5 VH\n if (dv_mph < 8) level = 0; else if (dv_mph < 12) level = 1; else if (dv_mph < 20) level = 2; else if (dv_mph < 30) level = 3; else if (dv_mph < 45) level = 4; else level = 5;\n\n // Vehicle/context modifiers\n if (restrained === \"belt+airbag\") level = Math.max(0, level - 1);\n if (restrained === \"unrestrained\") level = Math.min(5, level + 1);\n if (intrusion) level = Math.min(5, level + 1);\n if (dt_s && dt_s < 0.07) level = Math.min(5, level + 1); // short, severe pulse\n\n // Head-specific metrics (override upward only)\n if (typeof headG === \"number\" && headG >= 80) level = Math.max(level, 4);\n if (typeof HIC === \"number\" && HIC >= 700) level = Math.max(level, 5);\n if (typeof BrIC === \"number\" && BrIC >= 0.68) level = Math.max(level, 5);\n\n // --- Patient factors (heuristic, evidence-informed) ---\n // Age: adolescents and older adults may have elevated susceptibility/sequelae risk\n if (typeof age === 'number' && (age <= 16 || age >= 65)) level = Math.min(5, level + 1);\n // Sex: some studies report higher concussion incidence/severity in females (sports/auto)\n if (sex === 'female') level = Math.min(5, level + 1);\n // Genetic predisposition (user summarized):\n // none: 0; moderate (e.g., BDNF Val66Met, SLC6A4 short, COMT Val158Met): +1; high (e.g., APOE ε4 carrier or multiple risk alleles): +2\n if (genetic === 'moderate') level = Math.min(5, level + 1);\n if (genetic === 'high') level = Math.min(5, level + 2);\n\n const labels = [\"Very Low\",\"Low\",\"Low–Moderate\",\"Moderate\",\"High\",\"Very High\"]; const descs = [\n \"Very low proxy risk of mTBI based on crash, patient, and optional head metrics; does not rule out injury.\",\n \"Low proxy risk; symptoms can still occur—monitor clinically.\",\n \"Elevated proxy risk; consider clinical screening if symptomatic.\",\n \"Meaningful proxy risk; clinical evaluation for mTBI is reasonable.\",\n \"High proxy risk; strong consideration for clinical evaluation.\",\n \"Very high proxy risk; prompt clinical evaluation recommended.\",\n ];\n return { level, label: labels[level], message: descs[level] };\n}\n\nexport default function CrashForceAndMTBIRisk() {\n // Crash inputs\n const [dvValue, setDvValue] = useState(25);\n const [dvUnit, setDvUnit] = useState<\"mph\" | \"kmh\">(\"mph\");\n const [sValue, setSValue] = useState(1.5);\n const [sUnit, setSUnit] = useState<\"ft\" | \"m\">(\"ft\");\n const [dtValue, setDtValue] = useState(0.1);\n const [weightValue, setWeightValue] = useState(170);\n const [weightUnit, setWeightUnit] = useState<\"lbf\" | \"kg\">(\"lbf\");\n const [restrained, setRestrained] = useState(\"belt+airbag\");\n const [intrusion, setIntrusion] = useState(false);\n const [headG, setHeadG] = useState(\"\");\n const [hic, setHic] = useState(\"\");\n const [bric, setBric] = useState(\"\");\n\n // Patient factors\n const [age, setAge] = useState(38);\n const [sex, setSex] = useState<'male'|'female'|'other'|'unknown'>(\"male\");\n const [genetic, setGenetic] = useState<'none'|'moderate'|'high'>(\"none\");\n\n // Derived\n const dv_mph = useMemo(() => (dvUnit === \"mph\" ? dvValue : (dvValue || 0) * 0.621371), [dvValue, dvUnit]);\n const s_ft = useMemo(() => (sValue === \"\" ? undefined : sUnit === \"ft\" ? Number(sValue) : feetToMeters(Number(sValue)) / 0.3048), [sValue, sUnit]);\n const dt_s = useMemo(() => (dtValue === \"\" ? undefined : Number(dtValue)), [dtValue]);\n const weight_lbf = useMemo(() => { if (weightValue === \"\") return undefined; return weightUnit === \"lbf\" ? Number(weightValue) : newtonsToLbf((Number(weightValue) * g0)); }, [weightValue, weightUnit]);\n\n const g_distance = useMemo(() => gFromDistance(Number(dv_mph || 0), s_ft), [dv_mph, s_ft]);\n const g_time = useMemo(() => gFromTime(Number(dv_mph || 0), dt_s), [dv_mph, dt_s]);\n const g_best = g_time ?? g_distance; // prefer time if provided\n const force = useMemo(() => forceFromG(weight_lbf, g_best), [weight_lbf, g_best]);\n\n const risk = useMemo(() => riskGrade({\n dv_mph: Number(dv_mph || 0),\n restrained,\n intrusion,\n dt_s,\n headG: headG === \"\" ? undefined : Number(headG),\n HIC: hic === \"\" ? undefined : Number(hic),\n BrIC: bric === \"\" ? undefined : Number(bric),\n age: age === \"\" ? undefined : Number(age),\n sex,\n genetic,\n }), [dv_mph, restrained, intrusion, dt_s, headG, hic, bric, age, sex, genetic]);\n\n const riskTone = risk.level>=4? 'from-rose-50 to-violet-50 border-violet-200' : risk.level>=3? 'from-amber-50 to-indigo-50 border-amber-200' : 'from-emerald-50 to-indigo-50 border-emerald-200';\n\n return (\n

\n \n

NeuroGlympse — Crash Force & mTBI Risk

Enter crash and patient context to estimate average g, occupant force, and a proxy mTBI risk grade. Educational use only.

\n\n \n \n

Crash Inputs

\n ΔV (change in speed)\n

\n setDvValue(Number(e.target.value))} />\n \n

\n\n

\n Crash pulse duration (Δt, optional)\n

\n setDtValue(e.target.value === \"\" ? \"\" : Number(e.target.value))} />\n

\n\n

\n Ride-down distance (s, optional)\n

\n setSValue(e.target.value === \"\" ? \"\" : Number(e.target.value))} />\n \n

\n\n

\n Occupant weight\n

\n setWeightValue(e.target.value === \"\" ? \"\" : Number(e.target.value))} />\n \n

\n\n

\n Restraints\n \n

\n\n

\n Compartment intrusion\n \n

\n\n

\n Head linear accel (optional)\n

\n setHeadG(e.target.value === \"\" ? \"\" : Number(e.target.value))} />\n

\n\n

\n HIC (optional)\n setHic(e.target.value === \"\" ? \"\" : Number(e.target.value))} />\n

\n\n

\n BrIC (optional)\n setBric(e.target.value === \"\" ? \"\" : Number(e.target.value))} />\n

\n \n \n\n \n \n

Patient Factors

\n Age\n setAge(e.target.value === '' ? '' : Number(e.target.value))} />\n

\n Sex\n \n

\n Genetic predisposition\n \n

\n Genetic labels are a simplification for this prototype. In production, select variants explicitly (APOE ε4, BDNF Val66Met, COMT Val158Met, SLC6A4 5-HTTLPR), or import a genetic report to compute a weighted index.\n

\n \n

\n \n \n\n \n \n

Results

Avg g (time model)

{g_time ? `${round(g_time,2)} g` : \"—\"}

g = 0.0456 × ΔV(mph)/Δt(s)

Avg g (distance model)

{g_distance ? `${round(g_distance,2)} g` : \"—\"}

g = 0.0335 × ΔV(mph)^2 / s(ft)

\n\n

Estimated average force

{force.lbf ? `${round(force.lbf)} lbf` : \"—\"}

F≈Weight × g_avg (avg over pulse)

Force (newtons)

{force.N ? `${round(force.N)} N` : \"—\"}

\n\n

mTBI Proxy Grade

{risk.label}

{risk.message}

\n\n

Patient-factor adjustments: +1 for age ≤16 or ≥65; +1 for female sex; +1 for moderate genetics; +2 for high genetics. Caps at Very High. These are placeholders—replace with your validated weights once you fix the genetic panel and cohort analyses.

\n \n \n\n \n \n

How it works

Average deceleration is estimated with either a time-based model (a = Δv/Δt) or a distance-based model (a = Δv²/2s). We convert to g by dividing by g₀ and estimate average force on the occupant by Weight × g_avg. The mTBI proxy grade uses ΔV with crash/patient modifiers and can be raised by head metrics (linear g, HIC, BrIC). Patient factors (age, sex, genetics) are heuristic placeholders pending your validation.

\n\n

Key references (open links)

NHTSA: Head Injury Criterion (HIC) background & risk curves (Eppinger et al., 1999). PDF
NHTSA: Brain Injury Criterion (BrIC) development (Takhounts et al., 2013). PDF
Rowson et al., 2016: Concussive sports impacts ~98 ± 28 g; context for linear g. Open access
Baker et al., 2022: ΔV and pathological brain injury in road users; occupant threshold ~19 km/h. Open access

\n\n

\n NeuroGlympse™ heuristic estimator • v0.4 • For educational use only\n

\n \n \n

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