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Evaluation of language analysis to summarize the literature: a comparison to traditional meta-analysis in primary hip and knee surgery
  1. Joshua Myszewski1,
  2. Emily Klossowski2 and
  3. Kristopher M Schroeder3
  1. 1School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
  2. 2College of Letters and Science, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
  3. 3Anesthesiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
  1. Correspondence to Joshua Myszewski, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA; jmyszewski{at}wisc.edu

Abstract

Introduction Sentiment analysis, by evaluating written wording and its context, is a growing tool used in computer science that can determine the level of support expressed in a body of text using artificial intelligence methodologies. The application of sentiment analysis to biomedical literature is a growing field and offers the potential to rapidly and economically explore large amounts of published research and characterize treatment efficacy.

Methods We compared the results of sentiment analysis of 115 article abstracts analyzed in a recently published meta-analysis of peripheral nerve block usage in primary hip and knee arthroplasty to the conclusions drawn by the authors of the original meta-analysis.

Results A moderately positive outlook supporting the utilization of regional anesthesia for hip and knee arthroplasty was found in the 115 articles that were included for analysis, with 46% expressing positive sentiment, 35% expressing neutral sentiment, and 19% of abstracts expressing negative sentiment. This was well aligned with the conclusions reached by a previous meta-analysis of the same articles.

Discussion Sentiment analysis applied to the medical literature can rapidly evaluate large collections of published data and generate an impression of overall findings that are aligned with the findings of a traditional meta-analysis.

  • regional anesthesia
  • technology
  • methods

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Footnotes

  • Twitter @KristopherSchr6

  • Contributors JM performed analysis, data gathering, manuscript writing, and project conception. EK performed manuscript editing and writing, analysis, and interpretation of the results. KMS provided supervision, guidance and institutional support, performed manuscript writing, and assisted with project conception.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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