Description
Preserve explicit observed and adjudicated endpoint codes as distinct fields.
Statistical record calculations depend on risk-set boundaries, cluster assumptions, missingness codes and exposure definitions. Entry can be excluded while exit inclusion is a policy choice, and one design-effect formula assumes equal cluster sizes. A local flag or contribution does not fit a model or establish a clinical result.
Preserve explicit observed and adjudicated endpoint codes as distinct fields.
When to use this tool
Use Endpoint Observed Adjudicated Pair when you already have endpointId, observedCode, adjudicatedCode and need to preserve explicit observed and adjudicated endpoint codes as distinct fields.
How to use the tool and how it works
- Enter endpointId, observedCode, adjudicatedCode. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read token. Use the output convention below.
Enter endpointId, observedCode, adjudicatedCode using the field constraints below. Encode these values, in exactly that order, as a JSON array and return the serialized array as text in token. String values are preserved without trimming or external terminology lookup.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| endpointId | Endpoint Id | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| observedCode | Observed Code | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| adjudicatedCode | Adjudicated Code | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| token | Token | Required. Text; maximum UTF-16 length 8192. |
{
"input": {
"endpointId": "E17",
"observedCode": "positive",
"adjudicatedCode": "negative"
},
"output": {
"token": "[\"E17\",\"positive\",\"negative\"]"
}
}Limitations and assumptions
- The result records supplied values without reconciling clinical judgments. Sources and asserted relationships are not independently authenticated.
- Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
Alternative or Complementary methods
Use the complete study design, data and statistical model for inference. Check censoring, clustering, visit windows, exposure and missingness conventions before combining these terms, and assess uncertainty with the full analysis.
References
Similar or alternative tools
- Competing Event Cause Token
Record an explicit competing-event cause code.
- Person Visit Loss Follow Up Censoring Code
Preserve explicit study follow-up censoring metadata.