Dr. Nancy Agnes's presentation discusses the importance of data extraction in systematic reviews of prospective cohort studies, detailing the essential variables required for accurate analysis. The presentation highlights the transition from manual data extraction to the use of intelligent software for automation, emphasizing the design and testing of a structured data extraction form. Additionally, it covers the methodological items that should be evaluated, examples of medical survey data collection, and the role of Pubrica in facilitating comprehensive medical data collection services.

1. An Academic presentation by
Dr. Nancy Agnes, Head, Technical
Operations, Pubrica
Group: www.pubrica.com
Email: sales@pubrica.com
What are the Variables used in
data extraction for prospective
cohort studies in a systematic
review?

2. • Data extraction is an important stage in systematic reviews because it captures critical study features in
an organized and consistent format.
• It is required for analyzing bias risk and synthesizing results. Interventional, diagnostic, and prognostic
reviews take data from predetermined fields such as population, intervention, comparison, and results.
Data extraction by hand can be time-consuming and repetitive, but intelligent software allows for
automatic identification and medical data collection.
• This semi-automation integrates with EBM and data science, and interest in its research is expanding
alongside AI in other computer science domains.

3. • A data-extraction form based on crucial factors to prognosis to analyze the features of reviews and
primary research was designed and is accessible on request from the first author.
• Before the form was finished, it was pilot-tested by all review writers, and small changes were made
following a discussion of the disparities in scores. One narrative review author rated all reviews, while
others scored all reviews collectively.
• Consensus sessions were convened within two weeks of the review's completion to resolve differences. A
third reviewer was consulted to make the ultimate judgment if an agreement could not be achieved.
contd...

5. • An item was rated 'yes' if positive information on that specific methodological item was discovered, for
example, if it was obvious that sensitivity analyses were performed.
• If it was evident that a certain methodological condition was not met, a 'no' was assigned; for example,
no sensitivity analyses were performed. 'Unclear' was scored when there was a question or uncertainty.
• A methodological item may be graded as 'not applicable' at times. The proportion and quantity of
reviews within each answer category were given.
To know more about Medical Data collection Services, check our study guide. What are examples of medical survey data
collection?

6. • A data extraction tool in research is a software or systematic approach designed to gather relevant
information from various sources, such as research papers, databases, or surveys.
• When conducting a systematic review of prospective cohort studies, it's crucial to extract relevant data
from the included studies in a consistent and structured manner. Here are some variables to consider
when creating a data extraction form for your systematic review:
• Study characteristics: a) Study title b) Authors c) Publication year d) Journal e) Study location (country,
region) f) Study design (e.g., prospective cohort) g) Funding sources
• Population characteristics: a) Sample size b) Age range or mean age c) Sex distribution d) Ethnicity e)
Baseline health status (e.g., healthy, presence of specific conditions) f) Inclusion and exclusion criteria
contd...

7. • Exposure or risk factor assessment: a) Definition of exposure or risk factor b) Measurement method
(e.g., questionnaire, biomarker) c) Exposure categories or levels d) Frequency or duration of exposure e)
Confounding factors considered or adjusted for
• Outcome assessment: a) Definition of the outcome(s) of interest b) Measurement method (e.g., self-
report, medical records, death certificates) c) Duration of follow-up d) Incidence or prevalence of the
outcome(s) in exposed and non-exposed groups e) Loss to follow-up or attrition rate
• Results: a) Crude and adjusted effect estimates (e.g., hazard ratios, odds ratios, risk ratios) b)
Confidence intervals or standard errors c) Statistical significance (e.g., p-values) d) Subgroup analyses
or effect modification, if applicable
• Quality assessment: a) Newcastle-Ottawa Scale (NOS) score or another quality assessment tool b) Risk
of bias assessment, if applicable
Check our Medical data collection sample work to know and learn more about, Medical data collection on interstitial
cysts and drug uracyst's impact on patient quality of life.

8. • At Pubrica, we collect data from a wide range of sources and perform semantic annotation based
on the research questions that you wanted to solve.
• Pubrica has the vast majority of the data in doctor's notes; electronic medical records,
prescriptions, and similar information are available.
• Although therein lies the golden possibility of big data in medical care, it's challenging to yield
valuable insights due to complex, unstructured, longitudinal, and voluminous data.

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