To extract data for treatment arms or cohort groups, you can use nested tables.  Nested tables are essential for extracting:

• Baseline data for comparison across study groups (two or more).
• Outcome data related to treatment arms or cohorts for further analysis or review

A typical nested table for extracting outcome data includes the following sections:

• Rows, for example, Outcomes Name/Outcome Measure: In this field, you should extract the name and description of the evaluated outcomes or outcome measures.
  
• Columns, for example, study arms for which we will extract data. Columns are generated automatically, so first, you should finish the extraction of interventions in the study.
• Data for Study Arms: In this field, you should extract the specific data related to each study arm or treatment group. To extract data for the second study arm, simply switch to the treatment name within the nested table. This allows you to extract the outcome data specific to each study arm or treatment group separately.
• Connections Module: Here, you will connect data extracted within the nested table with other previously extracted data, such as follow-up or type of analysis

You can extract data manually or automatically with an AI model.

Manual extraction 

To extract outcome data for study arms, follow these steps:

1. Locate the outcome data in the PDF for the selected outcome

2. Select the cohort arm in the nested table for which you want to extract the data.

3. Highlight the outcome data on the PDF for the selected cohort arm.

4. Click the 'plus' button to add the data to the nested table.

To extract data for the second study arm, switch to the treatment name within the nested table. This allows you to extract the outcome data specific to each study arm or treatment group separately.

In a single nested table, you can extract data for more than two study arms if available in the extracted study. To add the next study group, enter the next treatment in the treatment arm section, the tool will automatically add the new cohort to the nested table.

Check if you have made the necessary connections with other extracted data, such as follow-up (the connection module can be found below the nested table). You may even start the extraction of outcome data with this module, similar to how it is done in an Excel sheet. First, define the follow-up or type of analysis for which you are starting to extract outcome data (in connection module), and then extract data for specific outcome and study arm

Automatic data extraction from table with AI model

To extract data with model support:

1. Click on the table from which you want to extract data and click on blue ‘Extract’ button.

In this example, our task is to extract outcome  data, including the name of the outcome (i.e., ACR70), the number of patients in cohorts, the number of patients with the event, and the percentage of patients with the event. 

2. Select from the list the type of field (outcome) you want to extract. 

In our example, it will be ‘outcomes’ 

3. Choose rows from tables which you want to extract.

In our example it will be specific outcomes you want to extract.

4. Define the type of data that belongs to each column. You will have to select from the list of fields in your data extraction form.

 In our example, the first field includes the outcome names.

The values that have been mapped and extracted from table are highlighted in yellow (see Extraction output in right side of the screen)

To one column, you can assign more than one data extraction field. This option will be useful in cases where one cell in the table includes two types of values, and you want to extract them separately to different fields in your extraction form. In our example, the second column includes two types of values: number of patients with the event and % of patients with the event. Select both field names from the list.  The order of selected field names represents the order of values in the cell.

5. You can extract additional data for all outcome objects  by adding them manually in Extraction output. Select the relevant data extraction field from the list and specify what should be extracted (add text/value).

In our example in the data extraction form, we have to extract the number of patients in each cohort. The same value should be extracted for all outcomes:

Enter value in extraction output and  tool will automatically add this term to all 'outcomes objects'. You can see how the model extracted these fields in the next screen. Those manually added values are here highlighted in blue. 

You can see how the model extracted data from table in the next screen. Please accept values extracted automatically by model. This can be done individually by clicking the "Accept" button, or you can use batch acceptance button 

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