Saylor Innovations Data Desk
Bayes calculator for a diagnostic test - positive and negative predictive value, likelihood ratios and a confusion matrix from prevalence, sensitivity and specificity
DataUnknowneip155:8453Exactvia facilitator:payAI
What is the chance i have itPositive predictive valueBayes theorem medical testFalse positive rate base rateLikelihood ratio
Calls · 30d
1
This endpoint's own trailing-30-day call count, as published by the upstream catalog and snapshotted daily. 9 snapshots so far.
$15.76
Verified settled volume
1,657 settlements proven x402 by their on-chain EIP-3009 marker.
$0.010
Listed price
As published in the catalog. Always read the live 402 before paying.
—
Calls · 30d
Upstream's own call count for this endpoint, not ours.
—
Unique payers · 30d
Never called
—
Upstream on-chain volume
Reported by the source catalog.
Paid to
0xA2A8dF0f7CaaBEa4e120a2568118f141569fc8aaThe wallet the 402 directs payment to. Its whole payment record — every payer, every chain — is on the merchant page.
Asset 0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913
Provider
The payTo wallet does not resolve to a registered ERC-8004 agent. That is not a verdict on the service — most of the catalog is unregistered.
Accepts
The payment requirements as published to the catalog. Read the live 402 before paying — a price here is a claim, not a quote.
0.01USDC≈ $0.01 USD
on Base · exact scheme
Pay 0.01 USDC on Base to 0xA2A8…fc8aa. The signed payment is good for 2 minutes.
- Paid to
- 0xA2A8…fc8aa
- USD Coin contract
- 0x8335…02913
- Payment window
- 2 minutes
- As published
- 10000 smallest units
The catalog’s raw entry
[
{
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"extra": {
"memo": "saylor:datadesk:bayes-diagnostic",
"name": "USD Coin",
"version": "2",
"facilitatorOnly": true,
"assetTransferMethod": "eip3009"
},
"payTo": "0xA2A8dF0f7CaaBEa4e120a2568118f141569fc8aa",
"amount": "10000",
"scheme": "exact",
"network": "eip155:8453",
"maxTimeoutSeconds": 120
}
]Extensions
{
"bazaar": {
"info": {
"input": {
"type": "http",
"method": "GET",
"queryParams": {
"prevalence_percent": "1",
"sensitivity_percent": "95",
"specificity_percent": "90"
}
},
"output": {
"type": "json",
"example": {
"note": "Bayes theorem with a single test and independent errors; real tests vary by population and are not medical advice.",
"inputs": {
"population": 100000,
"prevalence_percent": 1,
"sensitivity_percent": 95,
"specificity_percent": 90
},
"source": "Bayes theorem applied to prevalence, sensitivity and specificity",
"plain_language": "Of every 100 people who test positive, about 9 actually have the condition when prevalence is 1%.",
"confusion_matrix": {
"true_negatives": 89100,
"true_positives": 950,
"false_negatives": 50,
"false_positives": 9900
},
"false_discovery_percent": 91.244,
"negative_likelihood_ratio": 0.055556,
"positive_likelihood_ratio": 9.5,
"negative_predictive_value_percent": 99.944,
"positive_predictive_value_percent": 8.756,
"probability_of_positive_test_percent": 10.85,
"probability_disease_given_negative_percent": 0.0561
}
}
},
"schema": {
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"input"
],
"properties": {
"input": {
"type": "object",
"required": [
"type",
"method"
],
"properties": {
"type": {
"type": "string",
"const": "http"
},
"method": {
"enum": [
"GET"
],
"type": "string"
},
"queryParams": {
"type": "object",
"required": [
"prevalence_percent",
"sensitivity_percent",
"specificity_percent"
],
"properties": {
"prevalence_percent": {
"type": "string",
"description": "How common the condition is, percent"
},
"sensitivity_percent": {
"type": "string",
"description": "True positive rate, percent"
},
"specificity_percent": {
"type": "string",
"description": "True negative rate, percent"
}
}
}
},
"additionalProperties": false
},
"output": {
"type": "object",
"required": [
"type"
],
"properties": {
"type": {
"type": "string",
"const": "json"
},
"example": {
"type": "object",
"required": [
"inputs",
"positive_predictive_value_percent"
],
"properties": {
"note": {
"type": "string",
"description": "Caveat or context for reading the result"
},
"inputs": {
"type": "object",
"properties": {
"population": {
"type": [
"number",
"null"
],
"description": "Population size used for the confusion matrix"
},
"prevalence_percent": {
"type": [
"number",
"null"
],
"description": "Prevalence, as a percentage"
},
"sensitivity_percent": {
"type": [
"number",
"null"
],
"description": "Sensitivity, as a percentage"
},
"specificity_percent": {
"type": [
"number",
"null"
],
"description": "Specificity, as a percentage"
}
},
"description": "Inputs after validation, as percentages and the population size"
},
"source": {
"type": "string",
"description": "Data source the values come from"
},
"plain_language": {
"type": [
"string",
"null"
],
"description": "Plain language"
},
"confusion_matrix": {
"type": "object",
"description": "Confusion matrix"
},
"false_discovery_percent": {
"type": [
"number",
"null"
],
"description": "False discovery, as a percentage"
},
"negative_likelihood_ratio": {
"type": [
"number",
"null"
],
"description": "Negative likelihood ratio"
},
"positive_likelihood_ratio": {
"type": [
"number",
"null"
],
"description": "Positive likelihood ratio"
},
"negative_predictive_value_percent": {
"type": [
"number",
"null"
],
"description": "Negative predictive value, as a percentage"
},
"positive_predictive_value_percent": {
"type": [
"number",
"null"
],
"description": "Positive predictive value, as a percentage"
},
"probability_of_positive_test_percent": {
"type": "number",
"description": "Probability of positive test, as a percentage"
},
"probability_disease_given_negative_percent": {
"type": [
"number",
"null"
],
"description": "Probability disease given negative, as a percentage"
}
}
}
}
}
}
}
}
}Provenance
- Seen in the source catalog
- 2026-10-26 02:49Z
- Last indexed by Roundhouse
- 2026-10-26 20:15Z
- Last enriched (probe, favicon, geo)
- —
- x402 version
- 2
- Max timeout
- 120s
- Liveness probe
- Not probed
Hand this page to an agent
Copy the prompt and paste it into Claude, an MCP client or your own agent — it will vet this service and call it over the free read API. No key, no account.
GET api.roundhouseai.io/v0/endpoints
This endpoint's own trailing-30-day call count, as published by the upstream catalog and snapshotted daily. 9 snapshots so far. Verified volume counts only settlements with an on-chain EIP-3009 marker.
Show the promptHide the prompt
Using Roundhouse, look up the x402 service Saylor Innovations Data Desk and tell me whether it is worth paying: what a call costs, whether the endpoint answered when last probed, and what its payment record actually shows. curl -s 'https://api.roundhouseai.io/v0/endpoints?q=Saylor%20Innovations%20Data%20Desk' curl -s 'https://api.roundhouseai.io/v0/merchants/<the payTo wallet returned above>' Then call it: read the price from the live 402 at https://saylorinnovations.com/api/stats/bayes, never from a cached figure, and pay with an x402 client. The /v0 API needs an API key (`authorization: Bearer rh_live_…`) on everything except /v0/unified* and /v0/endpoints. Either pay over x402 (mint a personal key for $0.01 at GET https://api.roundhouseai.io/v0/test/x402), or ask me for an organization key, which I create at https://roundhouseai.io/dashboard/connections. If you have neither, ask before going further. If you do not have Roundhouse tools or skills installed, read https://roundhouseai.io/skill.md first — it is the whole procedure.