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Related Concept Videos

Archival Research01:40

Archival Research

16.0K
Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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Blind Procedures02:07

Blind Procedures

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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
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Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Related Experiment Video

Updated: Jul 9, 2025

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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Methods for using Bing's AI-powered search engine for data extraction for a systematic review.

James Edward Hill1, Catherine Harris1, Andrew Clegg1

  • 1Synthesis, Economic Evaluation and Decision Science (SEEDS) Group, University of Central Lancashire, Preston, UK.

Research Synthesis Methods
|December 9, 2023
PubMed
Summary
This summary is machine-generated.

This study explores using Bing AI as a second reviewer for data extraction in systematic reviews. AI can assist human reviewers, saving time and resources, but requires further validation before replacing traditional methods.

Keywords:
AIBingartificial intelligencedata extractionmachine learningsystematic review

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Systematic Review Methodology

Background:

  • Data extraction in systematic reviews is labor-intensive.
  • Natural Language Processing (NLP) and Artificial Intelligence (AI) offer automation potential.
  • Enhancing efficiency and reliability in systematic reviews is crucial.

Purpose of the Study:

  • To propose and demonstrate a method using Bing AI as a secondary reviewer for data extraction.
  • To evaluate the potential of AI in verifying and enhancing data extracted by human reviewers.
  • To provide a cost-effective solution for resource-limited systematic reviews.

Main Methods:

  • A worked example detailing the use of Bing AI Chat to extract study characteristics from PDF documents.
  • Instructing AI to populate data into a table for comparison with human-extracted data.
  • Utilizing Microsoft Edge as a platform for AI-assisted verification.

Main Results:

  • Bing AI can be instructed to extract specific data items from documents.
  • The AI-assisted method offers an additional layer of verification for data extraction.
  • This approach may be beneficial for reviewers with limited resources or experience.

Conclusions:

  • Bing AI shows promise as a supplementary tool for data extraction in systematic reviews.
  • It can enhance verification processes, especially when resources are scarce.
  • Further research is needed to validate AI's accuracy and efficiency against established double-extraction methods.