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Syst Rev. 2013 Nov 7;2:97. doi: 10.1186/2046-4053-2-97.

Data extraction from machine-translated versus original language randomized trial reports: a comparative study.

Author information

1
Tufts Evidence-based Practice Center, Institute for Clinical Research and Health Policy Studies, 800 Washington Street, Box 63, Boston, MA 02111, USA. ebalk@tuftsmedicalcenter.org.

Abstract

BACKGROUND:

Google Translate offers free Web-based translation, but it is unknown whether its translation accuracy is sufficient to use in systematic reviews to mitigate concerns about language bias.

METHODS:

We compared data extraction from non-English language studies with extraction from translations by Google Translate of 10 studies in each of five languages (Chinese, French, German, Japanese and Spanish). Fluent speakers double-extracted original-language articles. Researchers who did not speak the given language double-extracted translated articles along with 10 additional English language trials. Using the original language extractions as a gold standard, we estimated the probability and odds ratio of correctly extracting items from translated articles compared with English, adjusting for reviewer and language.

RESULTS:

Translation required about 30 minutes per article and extraction of translated articles required additional extraction time. The likelihood of correct extractions was greater for study design and intervention domain items than for outcome descriptions and, particularly, study results. Translated Spanish articles yielded the highest percentage of items (93%) that were correctly extracted more than half the time (followed by German and Japanese 89%, French 85%, and Chinese 78%) but Chinese articles yielded the highest percentage of items (41%) that were correctly extracted >98% of the time (followed by Spanish 30%, French 26%, German 22%, and Japanese 19%). In general, extractors' confidence in translations was not associated with their accuracy.

CONCLUSIONS:

Translation by Google Translate generally required few resources. Based on our analysis of translations from five languages, using machine translation has the potential to reduce language bias in systematic reviews; however, pending additional empirical data, reviewers should be cautious about using translated data. There remains a trade-off between completeness of systematic reviews (including all available studies) and risk of error (due to poor translation).

PMID:
24199894
PMCID:
PMC4226266
DOI:
10.1186/2046-4053-2-97
[Indexed for MEDLINE]
Free PMC Article

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