Data should be extracted based on previously identified interventions and outcomes developed during the formulation of the study topic, inclusion/exclusion requirements, and search procedure.
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Data should be extracted based on previously
identified interventions and outcomes developed
the formulation of
procedure. It should not be challenging to classify
the data elements that need to be retrieved from
each included sample if those phases have been
completed properly. To analyze and assess findings,
extract data from related studies. It is important to
use sound data collection techniques when the data
is being collected
. Data processing can begin as
soon as you begin collecting data, and it can even
determine which data types you retain.
overwhelmed by the volume of primary research
papers, both old and modern. Since it is currently
to scan for appropriate data with
accuracy, support for the early stages of the
systematic review phase – searching and screening
studies for eligibility – is needed. Not only could
better automatic data extraction help with the stage of
analysis known as "data extraction," but it could also
help with other aspects of the review process.
Systematic review (semi)automation research lies at
the intersection between evidence-based medicine
and computer science. Besides the advancement in
computing power and storage space, computers'
capacity to serve humans grows. Data extraction for
systematic analysis is a time-consuming process
opens up possibilities for sophisticated machines to
assist. In this domain, tools and methods are often
based on automating data processing relevant to the
Comparator, and Outcome).
Table 1 A summary of included extraction methods and their evaluation
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