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.

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?

What is a data extraction tool in research?

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. This tool streamlines the process by automating the extraction of specific data points, such as variables, outcomes, sample sizes, and methodologies. It ensures consistency, reduces manual effort, and enhances the efficiency of systematic literature reviews, meta-analyses, or any research requiring data synthesis. By facilitating the organization and categorization of data, these tools enable researchers to analyze, compare, and draw meaningful insights from a large volume of information, ultimately contributing to robust and evidence-based conclusions.

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:

1. 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
2. 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
3. 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
4. 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
5. 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
6. Quality assessment: a) Newcastle-Ottawa Scale (NOS) score or another quality assessment tool b) Risk of bias assessment, if applicable