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Google Translate can aid systematic reviews by reducing language bias, but caution is advised. While translation is efficient, accuracy varies by language and data type, posing a risk of errors.

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Area of Science:

  • Bibliometrics
  • Information Science
  • Medical Informatics

Background:

  • Google Translate is a free web-based tool.
  • Its accuracy for systematic reviews is not well-established.
  • Language bias is a concern in systematic reviews.

Purpose of the Study:

  • To assess Google Translate's accuracy for data extraction in systematic reviews.
  • To compare extraction accuracy from translated articles versus original English articles.
  • To evaluate the potential of machine translation in mitigating language bias.

Main Methods:

  • Compared data extraction from 50 non-English studies (10 per language: Chinese, French, German, Japanese, Spanish) with their Google Translate versions.
  • Fluent speakers double-extracted original articles; researchers without language proficiency double-extracted translated and English articles.
  • Used original language extractions as the gold standard to estimate extraction accuracy.

Main Results:

  • Translation took ~30 minutes per article; extraction from translated articles required more time.
  • Correct extraction likelihood was higher for study design/intervention items than for outcomes/results.
  • Spanish translations had the highest percentage of items correctly extracted >50% of the time (93%), while Chinese had the highest percentage of items correctly extracted >98% of the time (41%).

Conclusions:

  • Google Translate translation is resource-efficient.
  • Machine translation shows potential to reduce language bias in systematic reviews.
  • Caution is advised due to potential errors; a trade-off exists between review completeness and translation accuracy.