Scope of Use for This Data Extraction Form

This data extraction form is designed for use in reviews that aim to:

• Evaluate humanistic burden (e.g. quality of life, health state utility values)

• Create cataloque of HSUVs for inputs modeling

Overview of the Data Extraction Form

There are two categories of tabs in the Data Extraction Form:

1. General Tab

• Tab name: Study Details

• This tab is used to extract data regardless of the study type or the types of data included.

2. Specific Data Tabs

These tabs are designed to extract specific types of data, tailored to particular aspects of the review (e.g.,  utility values).

Data extraction process

Field: Results Group Label

Description: This section defines the population or study group to which the extracted results refer to. It should be completed before extracting the outcome/results data.

This section is highlighted in red.

Instructions: Before extracting any results, determine for what group(s) the data are presented in the study. Use this field to label the groups appropriately. This label should help clarify who the results apply to, and help distinguish between results for different study populations and/or treatments.

Examples of labels you can use:

• Main sample – if the results refer to the overall study population (e.g. patient with ulcerative colitis)

• Subgroup – [specify] – if the results refer to a subset of the main sample (e.g., “Subgroup – Females”, “Subgroup – UC mild”).

• Control sample– if the results refer to a comparator population (e.g. matched cohort-patient from the same hospital without disease).

• Treatment arms - Population defined by treatment

Case example: In this HRQoL study, there results are provided for main sample and subgropus definied by severity of disease . The names of these groups should be extracted first, as they will be used in the section dedicated to extracting HSUV.  

Look carefully at tables, figures, and captions to determine which population is being analyzed. 
If it's unclear, check the Methods or Results section for clarification.
The goal is to ensure each set of extracted results is clearly labeled by the group they belong to.

Vocabulary fields

Most of the data extraction fields are text fields, such as those used for extracting numerical values. However, some sections include vocabulary fields, where reviewers select data from a predefined list. During extraction, each reviewer has permission to create new codes if needed.

Database-format extraction

Please note that Laser AI supports a database-driven approach to data extraction. This means, for example, that different outcomes are extracted in separate sections. You won’t find dedicated sections for different halth states —instead, there is a single section called Results: HSUV. Within this section, you should select the specific health state you are extracting (e.g.,remission in the first subsection,flare in the second). 

Read more about this database approach 

Connections between PDF and values in data extraction forms

The best way to extract data in Laser AI is by using the highlighting mechanism. This method makes the quality assurance process easier, as clicking on a field with an extracted value redirects you to the corresponding section in the PDF where the value was extracted. To use this mechanism, you need to:

1.  Locate the specific data in the document and highlight it.
2.  Click the 'Plus' button (when empty data extraction fields exist)  to add the highlighted data to the corresponding data extraction field. In this way the relation between data in PDF and value in data extraction form will be established. When there are no empty fields in data extraction form, click the 'Plus' button located below the data extraction fields. The tool will create additional field and the extracted value will be automatically added to the data extraction form. Please note that this option is available only for sections with a single data extraction field 

Tabs description

Study details tab

This tab is dedicated to extracting study characteristics, such as study type, funding source, country, and more. 

There are two key sections that require your attention:

• Results Group Label
  
  

  • Read the description  in the previous chapter for guidance on how to define and use result groups.
    

• Patient Characteristics
  
  

Use this section to extract baseline characteristics of the study population.
It includes subsections to capture detailed demographic and clinical information for study arms.  Study arms in the subsections are automatically added once they are first extracted in the "Results Group Label" section. 

Recommended method: Use semi-automated table extraction where applicable.

Humanistic burden: HRQoL/HSUV tab

This tab is dedicated to extract primary HRQoL/Health state utility data.

It includes two sections:

• First section for Methods (highlighted in purple)
• One section for Results (highlighted in blue):

To extract data, you should label the type of data you are extracting: utility, disutility, or QoL

Recommended method: Use semi-automated table extraction where applicable.