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Created April 2, 2026 07:52
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Mike Lustgarten NHANES calculator
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mike-lustgarten-calculator.html
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Mike Lustgarten NHANES Calculator</title>
<style>
:root {
color-scheme: light dark;
--bg: #0b1020;
--panel: #121933;
--panel-2: #182247;
--text: #eaf0ff;
--muted: #9fb0d8;
--accent: #77a6ff;
--accent-2: #8cf0c4;
--border: rgba(255,255,255,0.08);
}
* { box-sizing: border-box; }
body {
margin: 0;
font-family: Inter, ui-sans-serif, system-ui, -apple-system, Segoe UI, Roboto, Arial, sans-serif;
background: linear-gradient(180deg, #0a0f1d 0%, #101833 100%);
color: var(--text);
}
.wrap { max-width: 1180px; margin: 0 auto; padding: 24px; }
.hero { margin-bottom: 20px; }
h1 { margin: 0 0 8px; font-size: 32px; }
.sub { color: var(--muted); line-height: 1.5; }
.grid { display: grid; grid-template-columns: 1.3fr 0.9fr; gap: 20px; }
.panel {
background: rgba(18, 25, 51, 0.92);
border: 1px solid var(--border);
border-radius: 18px;
padding: 20px;
backdrop-filter: blur(8px);
box-shadow: 0 10px 30px rgba(0,0,0,0.25);
}
.controls { display: flex; gap: 10px; flex-wrap: wrap; margin-bottom: 16px; }
button {
border: 0;
border-radius: 12px;
padding: 10px 14px;
background: var(--accent);
color: #08101f;
font-weight: 700;
cursor: pointer;
}
button.secondary { background: #2a396c; color: var(--text); }
.metrics { display: grid; grid-template-columns: 1fr 1fr; gap: 12px; margin-bottom: 18px; }
.metric { background: var(--panel-2); border-radius: 14px; padding: 14px; border: 1px solid var(--border); }
.metric .label { color: var(--muted); font-size: 12px; text-transform: uppercase; letter-spacing: 0.08em; }
.metric .value { font-size: 28px; font-weight: 800; margin-top: 6px; }
.metric .tiny { color: var(--muted); font-size: 12px; margin-top: 4px; }
.inputs { display: grid; grid-template-columns: repeat(3, minmax(0,1fr)); gap: 10px 12px; }
.field { background: var(--panel-2); border: 1px solid var(--border); border-radius: 12px; padding: 10px; }
.field label { display: block; font-size: 12px; color: var(--muted); margin-bottom: 6px; font-weight: 600; }
.field input { width: 100%; padding: 9px 10px; border-radius: 10px; border: 1px solid rgba(255,255,255,0.08); background: rgba(255,255,255,0.04); color: var(--text); font-size: 14px; }
.note { color: var(--muted); font-size: 13px; line-height: 1.5; }
code { color: var(--accent-2); }
.status { margin-top: 12px; color: var(--muted); font-size: 13px; }
@media (max-width: 980px) { .grid { grid-template-columns: 1fr; } .inputs { grid-template-columns: repeat(2, minmax(0,1fr)); } }
@media (max-width: 640px) { .inputs, .metrics { grid-template-columns: 1fr; } h1 { font-size: 26px; } .wrap { padding: 14px; } }
</style>
</head>
<body>
<div class="wrap">
<div class="hero">
<h1>Mike Lustgarten Calculator</h1>
<div class="sub">Single-page JavaScript calculator using the exact NHANES-trained Mike-specific model configuration from this workspace. Inputs are prefilled with Mike's panel and update the outputs instantly.</div>
</div>
<div class="grid">
<section class="panel">
<div class="controls">
<button id="resetBtn">Reset to Mike Baseline</button>
<button id="cholMinus10" class="secondary">TC -10%</button>
<button id="cholPlus10" class="secondary">TC +10%</button>
<button id="cholPlus50" class="secondary">TC +50%</button>
</div>
<div class="metrics">
<div class="metric"><div class="label">Risk Score</div><div class="value" id="riskScore">-</div><div class="tiny">Neural net output</div></div>
<div class="metric"><div class="label">Percentile</div><div class="value" id="percentile">-</div><div class="tiny">Among male NHANES reference</div></div>
<div class="metric"><div class="label">10y Risk</div><div class="value" id="risk10y">-</div><div class="tiny">Aging-related risk</div></div>
<div class="metric"><div class="label">Bio Age</div><div class="value" id="bioAge">-</div><div class="tiny">Risk-equivalent age</div></div>
</div>
<div class="inputs" id="inputs"></div>
</section>
<aside class="panel">
<h2 style="margin-top:0">Model Notes</h2>
<p class="note">Inference uses the exact one-hidden-layer MLP:</p>
<p class="note"><code>score(x) = w₂ᵀ GELU(W₁ ((x-μ)/σ) + b₁) + b₂</code></p>
<p class="note">Calibration maps <code>score</code> to 10-year aging-related risk and then to a risk-equivalent biological age on the male NHANES reference cohort.</p>
<div class="status">
<div>Features: <strong>27</strong></div>
<div>Validation C-index: <strong>0.8228</strong></div>
<div>Test C-index: <strong>0.8140</strong></div>
<div>Best step: <strong>200</strong></div>
<div>Training time: <strong>30.00s</strong></div>
</div>
</aside>
</div>
</div>
<script>
const MODEL = {"title": "Mike Lustgarten NHANES Calculator", "featureColumns": ["TCP", "HDP", "TGP", "CRP", "SGP", "BUP", "CEP", "NAPSI", "SKPSI", "CLPSI", "CAPSI", "TPP", "AMP", "TBP", "APPSI", "ASPSI", "ATPSI", "GHP", "WCP", "RCP", "HGP", "HTP", "MVPSI", "MCPSI", "MHP", "RWP", "LMPPCNT"], "baselineInputs": {"TCP": 137.0, "HDP": 58.0, "TGP": 65.0, "CRP": 0.02, "SGP": 90.0, "BUP": 12.0, "CEP": 0.83, "NAPSI": 138.0, "SKPSI": 4.3, "CLPSI": 102.0, "CAPSI": 2.4, "TPP": 7.4, "AMP": 5.1, "TBP": 0.5, "APPSI": 41.0, "ASPSI": 26.0, "ATPSI": 26.0, "GHP": 5.4, "WCP": 3.6, "RCP": 4.69, "HGP": 14.6, "HTP": 43.3, "MVPSI": 92.3, "MCPSI": 31.1, "MHP": 33.7, "RWP": 12.2, "LMPPCNT": 43.5}, "rawMean": [201.1949920654297, 47.23301696777344, 141.99652099609375, 0.42381781339645386, 101.14002227783203, 14.867018699645996, 1.1915419101715088, 141.5358123779297, 4.126547813415527, 104.26520538330078, 2.3119595050811768, 7.460105895996094, 4.231572151184082, 0.7132540345191956, 90.1558837890625, 24.446060180664062, 21.929546356201172, 5.507958889007568, 6.976778030395508, 4.990982532501221, 15.062668800354004, 44.628116607666016, 89.6776351928711, 30.26770782470703, 33.743408203125, 13.133132934570312, 32.487586975097656], "rawStd": [42.52330017089844, 14.075785636901855, 127.67306518554688, 0.7872707843780518, 27.91973114013672, 5.162662029266357, 0.3367239534854889, 2.345432996749878, 0.32396841049194336, 3.22438907623291, 0.10819879919290543, 0.45306336879730225, 0.3439047932624817, 0.35106876492500305, 32.78130340576172, 14.85720157623291, 17.57682991027832, 0.9452552199363708, 2.192653179168701, 0.438852995634079, 1.2069309949874878, 3.319495677947998, 5.139676094055176, 2.003187894821167, 0.8336448669433594, 1.0323991775512695, 8.803284645080566], "w1": [[0.29071059823036194, 0.2963907718658447, -0.09197390824556351, 0.2667030394077301, 0.010461722500622272, 0.10579055547714233, -0.196896031498909, 0.12001346051692963, 0.17121897637844086, 0.12861722707748413, 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function gelu(x) {
return 0.5 * x * (1 + Math.tanh(Math.sqrt(2 / Math.PI) * (x + 0.044715 * Math.pow(x, 3))));
}
function computeScore(inputs) {
const z = MODEL.featureColumns.map((name, i) => ((Number(inputs[name]) - MODEL.rawMean[i]) / MODEL.rawStd[i]));
const hidden = MODEL.w1.map((row, j) => {
let sum = MODEL.b1[j];
for (let i = 0; i < row.length; i++) sum += row[i] * z[i];
return gelu(sum);
});
let out = MODEL.b2;
for (let j = 0; j < hidden.length; j++) out += MODEL.w2[j] * hidden[j];
return out;
}
function riskAt120m(score) {
const val = MODEL.scoreBaselineCumHazardAt120m * Math.exp(Math.max(-50, Math.min(50, MODEL.scoreBeta * score)));
return 1 - Math.exp(-val);
}
function bioAgeFromRisk(risk) {
const p = Math.min(Math.max(risk, 1e-12), 1 - 1e-12);
const inner = -Math.log1p(-p) / Math.max(MODEL.ageBaselineCumHazardAt120m, 1e-12);
return Math.log(Math.max(inner, 1e-300)) / MODEL.ageBeta;
}
function percentile(sorted, value) {
let lo = 0, hi = sorted.length;
while (lo < hi) {
const mid = (lo + hi) >> 1;
if (sorted[mid] <= value) lo = mid + 1; else hi = mid;
}
return (100 * lo) / sorted.length;
}
const inputsEl = document.getElementById('inputs');
const fields = {};
function renderInputs(values) {
inputsEl.innerHTML = '';
MODEL.featureColumns.forEach((name) => {
const field = document.createElement('div');
field.className = 'field';
field.innerHTML = `<label for="${name}">${name}</label><input id="${name}" type="number" step="any" value="${values[name]}">`;
inputsEl.appendChild(field);
const input = field.querySelector('input');
input.addEventListener('input', update);
fields[name] = input;
});
}
function currentInputs() {
const out = {};
for (const name of MODEL.featureColumns) out[name] = Number(fields[name].value);
return out;
}
function update() {
const inputs = currentInputs();
const score = computeScore(inputs);
const pctl = percentile(MODEL.allReferenceScoresSorted, score);
const risk = riskAt120m(score);
const bioAge = bioAgeFromRisk(risk);
document.getElementById('riskScore').textContent = score.toFixed(4);
document.getElementById('percentile').textContent = `${pctl.toFixed(1)}th`;
document.getElementById('risk10y').textContent = `${(risk * 100).toFixed(2)}%`;
document.getElementById('bioAge').textContent = bioAge.toFixed(1);
}
document.getElementById('resetBtn').addEventListener('click', () => {
for (const [k, v] of Object.entries(MODEL.baselineInputs)) fields[k].value = v;
update();
});
document.getElementById('cholMinus10').addEventListener('click', () => { fields.TCP.value = (MODEL.baselineInputs.TCP * 0.9).toFixed(2); update(); });
document.getElementById('cholPlus10').addEventListener('click', () => { fields.TCP.value = (MODEL.baselineInputs.TCP * 1.1).toFixed(2); update(); });
document.getElementById('cholPlus50').addEventListener('click', () => { fields.TCP.value = (MODEL.baselineInputs.TCP * 1.5).toFixed(2); update(); });
renderInputs(MODEL.baselineInputs);
update();
</script>
</body>
</html>
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