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Trial Registry Records - Unit Label Consistency
x402
Trial Registry Records - Unit Label Consistency is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.012333/call, status unknown (last checked 2026-10-25).
Validates that declared aggregate count kinds match unit labels within a single trial registry record (source/work/study/revision).
Price / call$0.012333
Success—
StatusUnknown
How this service works
Check declared aggregate count kinds against unit labels in one exact source/work/study/revision Supplied-record analysis, not independent certification. Full contract at the free describe URL.
Output
Returns an analysis of whether declared aggregate count kinds are consistent with unit labels in the supplied record, including identified mismatches or confirmation of consistency. This is a supplied-record analysis, not independent certification.
Example use cases
Clinical trial submission pre-check
“Before we submit this trial registry record, can you validate that all the aggregate count kinds in our 8 public measurement rows are consistent with their unit labels? Flag any mismatches so we can fix them.”
Audit synthetic trial dataset units
“I have a synthetic dataset with 15 measurement rows from a study revision — please check whether the declared aggregate count kinds match the unit labels throughout the record and tell me where they conflict.”
Research QA pipeline unit validation
“As part of our QA pipeline, run a unit label consistency check on this public trial registry record with 5 measurement entries and confirm whether the aggregate count declarations line up with the labeled units.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/intel/trial-registry-records/unit-label-consistency?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to audit unit label consistency specifically within a single trial registry record (source/work/study/revision context) and your data is either public or synthetic. It is purpose-built for aggregate count kind vs. unit label cross-checking in trial registry contexts, unlike general schema validators or data quality tools that lack domain-specific unit semantics.
Side effects
- measurements array empty or exceeds 32 items — rejected with validation error
- nonpersonalMetadata not set to true — request refused
- dataClass not one of 'public' or 'synthetic' — enum validation failure
- malformed measurement objects in array — parsing error
- ambiguous unit labels that cannot be resolved — partial or failed analysis
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| dataClass | string | |
| measurements | array | |
| nonpersonalMetadata | boolean |
Tags
x402
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