Systematic reviews have studied rather than reports as the unit of interest. So, multiple reports of the same study need to be identified and linked together before or after data extraction. Because of the growing abundance of data sources (e.g., studies registers, regulatory records, and clinical research reports), review writers can determine which sources can include the most relevant details for the review and provide a strategy in place to address discrepancies if evidence were inconsistent throughout sources(1). The key to effective data collection is creating simple forms and gathering enough clear data that accurately represents the source in a formal and ordered manner.
The systematic review is designed to find all experiments applicable to their research question and synthesize data about the design, probability of bias, and outcomes of those studies. As a result, decisions on how to present and analyze data from these studies significantly impact a systematic review. Data collected should be reliable, complete, and available for future updating and data sharing (2). The methods used to make these choices must be straightforward, and they should be selected with biases and human error in mind. We define data collection methods used in a systematic review, including data extraction directly from journal articles and other study papers.
One scientist extracted the characteristics and findings of the observational cohort studies. The mainobjectives of each scientific analysis were also derived, and the studies were divided into two groups based on whether they dealt with biased reporting or source discrepancies. When the published results were chosen from different analyses of the same data with a given result, this was referred to as selective analysis reporting. When information was missing in one source but mentioned in another, or when the information provided in two sources was conflicting, a discrepancy was identified. Another author double-checked the data extraction. There was no masking, and disputes were settled by conversation (3).
Table: 1 Example for data extraction in the systemic review
They established and used three criteria to determine methodological quality because there was no recognized tool to evaluate the empirical studies’ organizational quality.
For each study, two authors independently evaluated these things. Since the first author was personally involved in the study’s design, an independent assessor was invited to review it. Any discrepancies were resolved through a consensus discussion with a third reviewer who was not concerned with the included studies (5).
Data extraction mistakes are extremely common. It may lead to significant bias in impact estimates. However, few studies have been conducted on the impact of various data extraction methods, reviewer characteristics, and reviewer training on data extraction quality. As a result, the evidence base for existing data extraction criteria appears to be lacking because the actual benefit of a particular extraction process (e.g. independent data extraction) or the composition of the extraction team (e.g. experience) has not been adequately demonstrated. It is unexpected, considering that data extraction is such an important part of a systematic review. More comparative studies are required to gain a better understanding of the impact of various extraction methods. Studies on data extraction training, in particular, are required because no such work has been done to date. In the future, expanding one’s knowledge base will aid in the development of successful training methods for new reviewers and students (6).