Completeness check

This type of validation is performed by the tool once you click the 'Finalize' button after completing the extraction.

There are four categories of errors that the tool will check:

1. Not verified suggestions - if you want accept or reject AI model suggestions or, in the case of the QA stage, values extracted by the first reviewer, the tool will treat is as an error that blocks further actions
2. Non-coded values - if there are both models and vocabs enabled, it may happened that value extracted by AI model will not be matched with the vocabulary term. In this case, after the acceptance of the model suggestion, there is still an action to be done - if you see that  the field is still yellow - you have to match the suggestion with the vocabulary or create a new vocabulary term. Otherwise, the tool won't treat it as a filled field. 
3. Missing connections
4. Empty fields 

All errors are presented separately for each tab. To correct extracted data, click on the relevant type of error, and you will be redirected to the data extraction form. 

Two types of errors prevent finalizing data extraction:
      -Not verified suggestions (AI model suggestions or, in the case of the QA stage, values extracted by the first reviewer)
     - Empty required fields.
Tabs with these errors will be highlighted in red. However, you have the option to automatically accept or reject suggestions or mark all empty required fields as 'not reported.'

When there are no significant errors in the tabs (now marked in blue), you will see the remaining missing items. You have two options here: correct each error or save anyway.

RELATED ARTICLES

1. Validation of extracted data - introduction  
2. Quality assurance stage  
3. Modification of extracted data on Reference list