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Saylor Innovations Data Desk – Bayesian Diagnostic Test Calculator
x402
Saylor Innovations Data Desk – Bayesian Diagnostic Test Calculator is a paid API for AI agents from saylorinnovations.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-21).
Calculates positive/negative predictive values, likelihood ratios, false discovery rate, and a confusion matrix for a diagnostic test given prevalence, sensitivity, and specificity
Price / call$0.01
Success—
StatusUnknown
How this service works
Bayes calculator for a diagnostic test - positive and negative predictive value, likelihood ratios and a confusion matrix from prevalence, sensitivity and specificity
Output
Returns a JSON object with validated inputs, positive predictive value (percent), negative predictive value (percent), positive and negative likelihood ratios, false discovery rate (percent), a confusion matrix showing true/false positives and negatives, a plain-language summary, and optional notes providing clinical context.
Example use cases
Evaluating COVID screening test reliability
“For a COVID rapid test with 8% population prevalence, 85% sensitivity, and 97% specificity, what are the positive and negative predictive values and how many false positives would I expect?”
Assessing rare disease genetic test accuracy
“I'm looking at a genetic test for a condition with 0.5% prevalence, 99% sensitivity, and 99% specificity — can you calculate the PPV, NPV, and likelihood ratios so I know how much to trust a positive result?”
Calibrating cancer screening program
“Our mammography screening program has a 1.2% prevalence in the target population, 80% sensitivity, and 90% specificity — run the Bayes numbers and give me the confusion matrix and plain-language interpretation.”
Technical details
Endpoint
GEThttps://saylorinnovations.com/api/stats/bayes?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, structured Bayesian diagnostic test statistics — PPV, NPV, likelihood ratios, and confusion matrix — given known prevalence, sensitivity, and specificity. It is ideal for medical informatics, epidemiology agents, clinical decision support, or any workflow evaluating the real-world accuracy of a binary diagnostic test. Prefer it over generic statistics libraries when you need a single API call with plain-language output included.
Side effects
- Missing required query parameters (prevalence_percent, sensitivity_percent, specificity_percent) returns an error
- Values outside valid range (e.g. >100 or negative percentages) may return validation errors or null fields
- Extreme edge cases (0% prevalence or 100% specificity) may produce undefined likelihood ratios
- Non-numeric string values for percent fields will cause parsing failures
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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