Agent Service · x402 · early usage

Carmona-Bayonas 2017 EPIPHANY CHAID tree for cancer-associated PE

by api.magentlab.com · discovered, not yet claimed by its owner · updated 2026-10-13

30d calls5

30d payers2

MethodPOST

Outputjson

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

Carmona-Bayonas 2017 EPIPHANY CHAID tree for cancer-associated PE. Hestia-like CDR any vs none, then ECOG-PS, SaO2, PE symptoms, tumour response, and primary resection as in Figure 2. Returns low, intermediate, or high with 15-day complication rates 1.6/9.4/30.6% and mortality 0.3/6.1/17.1%. Caller assigns the six covariates. Not a diagnosis or anticoagulation order. magent does not examine the patient.

Tags: epiphany · pe · cancer · vte · calculator · bazaar

Call contract

Method: POST
Input: HTTP request
Output: json
Last paid call: 2026-10-13

Input example from Bazaar metadata

{
  "items": [
    {
      "ecog_ps_ge_2": false,
      "hestia_like_any": false,
      "oxygen_sat_lt_90": false,
      "pe_specific_symptoms": false,
      "primary_tumour_resected": true,
      "tumour_uncontrolled": false
    },
    {
      "ecog_ps_ge_2": false,
      "hestia_like_any": false,
      "oxygen_sat_lt_90": false,
      "pe_specific_symptoms": false,
      "primary_tumour_resected": true,
      "tumour_uncontrolled": false
    }
  ]
}

Output example

{
  "count": 2,
  "items": [
    {
      "citation": {
        "authors": "Carmona-Bayonas A, Jiménez-Fonseca P, Font C, Fenoy F, Otero R, Beato C, Plasencia JM, Biosca M, Sánchez M, Benegas M, Calvo-Temprano D, Varona D, Faez L, de la Haba I, Antonio M, Madridano O, Solis MP, Ramchandani A, Castañón E, Marchena PJ, Martín M, Ayala de la Peña F, Vicente V; Asociación de Investigación de la Enfermedad Tromboembólica de la Región de Murcia",
        "doi": "10.1038/bjc.2017.48",
        "id": "carmona-bayonas-2017",
        "pmid": "28267709",
        "source": "Br J Cancer. 2017;116:994-1001",
        "title": "Predicting serious complications in patients with cancer and pulmonary embolism using decision tree modelling: the EPIPHANY Index"
      },
      "criteria": [
        {
          "factor": "hestia_like_any",
          "met": false
        },
        {
          "factor": "ecog_ps_ge_2",
          "met": false
        },
        {
          "factor": "oxygen_sat_lt_90",
          "met": false
        },
        {
          "factor": "pe_specific_symptoms",
          "met": false
        },
        {
          "factor": "tumour_uncontrolled",
          "met": false
        },
        {
          "factor": "primary_tumour_resected",
          "met": true
        }
      ],
      "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.",
      "ecog_ps_ge_2": false,
      "formula": "epiphany",
      "formula_expression": "root: hestia-like CDR any vs none (Carmona-Bayonas 2017 Figure 2). CDR any: ECOG-PS >=2 then SaO2 <90 vs >=90 (both high), else PE-specific symptoms yes (high) vs no (intermediate). CDR none: tumour progression/unknown/not evaluated vs others (CR/PR/SD/NED); if uncontrolled then primary tumour resection yes (low) vs no (intermediate); if others then low. Classes low/intermediate/high with 15-day serious complications 1.6/9.4/30.6% and 15-day mortality 0.3/6.1/17.1%",
      "hestia_like_any": false,
      "leaf": "low_controlled",
      "mortality_15d_percent": 0.3,
      "oxygen_sat_lt_90": false,
      "pe_specific_symptoms": false,
      "primary_tumour_resected": true,
      "risk_class": "low",
      "serious_complication_15d_percent": 1.6,
      "tumour_uncontrolled": false
    },
    {
      "citation": {
        "authors": "Carmona-Bayonas A, Jiménez-Fonseca P, Font C, Fenoy F, Otero R, Beato C, Plasencia JM, Biosca M, Sánchez M, Benegas M, Calvo-Temprano D, Varona D, Faez L, de la Haba I, Antonio M, Madridano O, Solis MP, Ramchandani A, Castañón E, Marchena PJ, Martín M, Ayala de la Peña F, Vicente V; Asociación de Investigación de la Enfermedad Tromboembólica de la Región de Murcia",
        "doi": "10.1038/bjc.2017.48",
        "id": "carmona-bayonas-2017",
        "pmid": "28267709",
        "source": "Br J Cancer. 2017;116:994-1001",
        "title": "Predicting serious complications in patients with cancer and pulmonary embolism using decision tree modelling: the EPIPHANY Index"
      },
      "criteria": [
        {
          "factor": "hestia_like_any",
          "met": false
        },
        {
          "factor": "ecog_ps_ge_2",
          "met": false
        },
        {
          "factor": "oxygen_sat_lt_90",
          "met": false
        },
        {
          "factor": "pe_specific_symptoms",
          "met": false
        },
        {
          "factor": "tumour_uncontrolled",
          "met": false
        },
        {
          "factor": "primary_tumour_resected",
          "met": true
        }
      ],
      "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.",
      "ecog_ps_ge_2": false,
      "formula": "epiphany",
      "formula_expression": "root: hestia-like CDR any vs none (Carmona-Bayonas 2017 Figure 2). CDR any: ECOG-PS >=2 then SaO2 <90 vs >=90 (both high), else PE-specific symptoms yes (high) vs no (intermediate). CDR none: tumour progression/unknown/not evaluated vs others (CR/PR/SD/NED); if uncontrolled then primary tumour resection yes (low) vs no (intermediate); if others then low. Classes low/intermediate/high with 15-day serious complications 1.6/9.4/30.6% and 15-day mortality 0.3/6.1/17.1%",
      "hestia_like_any": false,
      "leaf": "low_controlled",
      "mortality_15d_percent": 0.3,
      "oxygen_sat_lt_90": false,
      "pe_specific_symptoms": false,
      "primary_tumour_resected": true,
      "risk_class": "low",
      "serious_complication_15d_percent": 1.6,
      "tumour_uncontrolled": false
    }
  ],
  "path": "/api/calc/epiphany"
}

Use with an agent

Discover. Agents find this listing with GET https://bazaar.saylorinnovations.com/discovery/search?query=…, the MCP tool search_resources, or /discovery/resources.
Inspect. Fetch /resources/39188.json for the price and payment options.
Pay. Call GET https://api.magentlab.com/api/calc/epiphany. The provider answers 402 with its payment requirements. An x402 client signs one of the options below and retries with PAYMENT-SIGNATURE.
Execute. The provider returns 200 with 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

curl "https://api.magentlab.com/api/calc/epiphany"
# -> 402 Payment Required, accepts[] lists how to pay
# retry with a PAYMENT-SIGNATURE (or PAYMENT header) once paid

JavaScript

const res = await fetch("https://api.magentlab.com/api/calc/epiphany");
if (res.status === 402) {
  const { accepts } = await res.json();
  // pay one of accepts[] via an x402 client, then retry with the payment header
}

Python

import httpx
res = httpx.get("https://api.magentlab.com/api/calc/epiphany")
if res.status_code == 402:
    accepts = res.json()["accepts"]
    # pay one of accepts[] via an x402 client, then retry with the payment header

Details

Provider: api.magentlab.com
Endpoint: https://api.magentlab.com/api/calc/epiphany
Machine-readable: /resources/39188.json