Lima 2016 FAST-ED (Field Assessment Stroke Triage for Emergency Destination): facial_pa…
Lima 2016 FAST-ED (Field Assessment Stroke Triage for Emergency Destination): facial_palsy 0-1 + arm_weakness 0-2 + speech 0-2 + eye_deviation 0-2 + neglect 0-2. Max 9. lvo_suggested iff score ≥4. Bands 0_to_1 / 2_to_3 / gte_4. Time is documented for decision…
by api.magentlab.com · discovered, not yet claimed by its owner · updated 2026-10-13
Mirrored from Coinbase's public x402 Bazaar. The description is the provider's own text and has not been reviewed here; the payment requirements are copied as published.
What it does
Lima 2016 FAST-ED (Field Assessment Stroke Triage for Emergency Destination): facial_palsy 0-1 + arm_weakness 0-2 + speech 0-2 + eye_deviation 0-2 + neglect 0-2. Max 9. lvo_suggested iff score ≥4. Bands 0_to_1 / 2_to_3 / gte_4. Time is documented for decision making (0 points, not scored). Not a stroke diagnosis or transfer order. magent does not examine the patient.
Tags: fast-ed · stroke · lvo · prehospital · neurology · bazaar
Call contract
- Method
- POST
- Input
- HTTP request
- Output
- json
- Last paid call
- 2026-10-13
{
"items": [
{
"arm_weakness": 0,
"eye_deviation": 0,
"facial_palsy": 0,
"neglect": 0,
"speech": 0
},
{
"arm_weakness": 0,
"eye_deviation": 0,
"facial_palsy": 0,
"neglect": 0,
"speech": 0
}
]
}{
"count": 2,
"items": [
{
"arm_weakness": 0,
"citation": {
"authors": "Lima FO, Silva GS, Furie KL, et al.",
"doi": "10.1161/STROKEAHA.116.013301",
"id": "lima-2016",
"pmid": "27364531",
"source": "Stroke. 2016;47(8):1997-2002",
"title": "Field Assessment Stroke Triage for Emergency Destination: A Simple and Accurate Prehospital Scale to Detect Large Vessel Occlusion Strokes"
},
"components": [
{
"factor": "facial_palsy",
"points": 0,
"present": false,
"value": 0
},
{
"factor": "arm_weakness",
"points": 0,
"present": false,
"value": 0
},
{
"factor": "speech",
"points": 0,
"present": false,
"value": 0
},
{
"factor": "eye_deviation",
"points": 0,
"present": false,
"value": 0
},
{
"factor": "neglect",
"points": 0,
"present": false,
"value": 0
}
],
"disclaimer": "Not a medical device. Deterministic published-formula or published-code output for autonomous agents. A licensed clinician remains responsible for patient care and billing submissions.",
"eye_deviation": 0,
"facial_palsy": 0,
"fast_ed_band": "0_to_1",
"formula": "fast_ed",
"formula_expression": "FAST-ED = facial_palsy (0 normal or minor, 1 partial or complete) + arm_weakness (0 no drift, 1 drift or some effort against gravity, 2 no effort against gravity or no movement) + speech (0 absent, 1 mild to moderate, 2 severe, global aphasia, or mute) + eye_deviation (0 absent, 1 partial, 2 forced) + neglect (0 absent, 1 extinction to bilateral stimulation in only one sensory modality, 2 does not recognize own hand or orients only to one side); Lima 2016 Table 1; Time documented for decision making (0 points, not scored); max 9; lvo_suggested if score ≥ 4; fast_ed_band 0_to_1 (score 0–1, <15% LVO), 2_to_3 (score 2–3, ~30%), gte_4 (score ≥4, ~60%+)",
"lvo_suggested": false,
"max_score": 9,
"neglect": 0,
"score": 0,
"speech": 0
},
{
"arm_weakness": 0,
"citation": {
"authors": "Lima FO, Silva GS, Furie KL, et al.",
"doi": "10.1161/STROKEAHA.116.013301",
"id": "lima-2016",
"pmid": "27364531",
"source": "Stroke. 2016;47(8):1997-2002",
"title": "Field Assessment Stroke Triage for Emergency Destination: A Simple and Accurate Prehospital Scale to Detect Large Vessel Occlusion Strokes"
},
"components": [
{
"factor": "facial_palsy",
"points": 0,
"present": false,
"value": 0
},
{
"factor": "arm_weakness",
"points": 0,
"present": false,
"value": 0
},
{
"factor": "speech",
"points": 0,
"present": false,
"value": 0
},
{
"factor": "eye_deviation",
"points": 0,
"present": false,
"value": 0
},
{
"factor": "neglect",
"points": 0,
"present": false,
"value": 0
}
],
"disclaimer": "Not a medical device. Deterministic published-formula or published-code output for autonomous agents. A licensed clinician remains responsible for patient care and billing submissions.",
"eye_deviation": 0,
"facial_palsy": 0,
"fast_ed_band": "0_to_1",
"formula": "fast_ed",
"formula_expression": "FAST-ED = facial_palsy (0 normal or minor, 1 partial or complete) + arm_weakness (0 no drift, 1 drift or some effort against gravity, 2 no effort against gravity or no movement) + speech (0 absent, 1 mild to moderate, 2 severe, global aphasia, or mute) + eye_deviation (0 absent, 1 partial, 2 forced) + neglect (0 absent, 1 extinction to bilateral stimulation in only one sensory modality, 2 does not recognize own hand or orients only to one side); Lima 2016 Table 1; Time documented for decision making (0 points, not scored); max 9; lvo_suggested if score ≥ 4; fast_ed_band 0_to_1 (score 0–1, <15% LVO), 2_to_3 (score 2–3, ~30%), gte_4 (score ≥4, ~60%+)",
"lvo_suggested": false,
"max_score": 9,
"neglect": 0,
"score": 0,
"speech": 0
}
],
"path": "/api/calc/fast_ed"
}Use with an agent
- Discover. Agents find this listing with
GET https://bazaar.saylorinnovations.com/discovery/search?query=…, the MCP toolsearch_resources, or/discovery/resources. - Inspect. Fetch
/resources/39180.jsonfor the price and payment options. - Pay. Call
GET https://api.magentlab.com/api/calc/fast_ed. The provider answers402with its payment requirements. An x402 client signs one of the options below and retries withPAYMENT-SIGNATURE. - Execute. The provider returns
200with the data. Agent Bazaar is not in the request path and never sees your payment.
For humans
You don't need an account or an API key; you need a wallet holding a small amount of USDC on Base, and an x402-capable client (see the examples below). Call the endpoint unpaid first to see exactly what it asks for.
Pricing & payment
- Price
- $0.005 / request
- Protocol
- x402 v2
- Auth
- No account or API key. Pay per request.
- Networks
- Base
- Demand
- 5 calls · 2 unique payers in 30d (public x402 Bazaar data)
x402 payment requirements
| Scheme | Network | Asset | Amount | Pay to | Timeout |
|---|---|---|---|---|---|
| exact | Base eip155:8453 | USDC | $0.005 | 0x836Be278f1739fB4308091cD1852F7971Bc19Ee2 | 600s |
accepts[] (raw JSON)
[
{
"scheme": "exact",
"network": "eip155:8453",
"payTo": "0x836Be278f1739fB4308091cD1852F7971Bc19Ee2",
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"amount": "5000",
"amountUsd": 0.005,
"maxTimeoutSeconds": 600
}
]Schemas
Output schema (JSON Schema)
{
"properties": {
"example": {
"type": "object"
},
"type": {
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
}Example shape — generated from the schema, not a captured live response
{
"example": {},
"type": "string"
}Examples
curl "https://api.magentlab.com/api/calc/fast_ed"
# -> 402 Payment Required, accepts[] lists how to pay
# retry with a PAYMENT-SIGNATURE (or PAYMENT header) once paidconst res = await fetch("https://api.magentlab.com/api/calc/fast_ed");
if (res.status === 402) {
const { accepts } = await res.json();
// pay one of accepts[] via an x402 client, then retry with the payment header
}import httpx
res = httpx.get("https://api.magentlab.com/api/calc/fast_ed")
if res.status_code == 402:
accepts = res.json()["accepts"]
# pay one of accepts[] via an x402 client, then retry with the payment headerDetails
- Provider
- api.magentlab.com
- Endpoint
- https://api.magentlab.com/api/calc/fast_ed
- Machine-readable
/resources/39180.json